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Liu S, Chen J, Guan L, Xu L, Cai H, Wang J, Zhu DM, Zhu J, Yu Y. The brain, rapid eye movement sleep, and major depressive disorder: A multimodal neuroimaging study. Prog Neuropsychopharmacol Biol Psychiatry 2024:111151. [PMID: 39326695 DOI: 10.1016/j.pnpbp.2024.111151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 09/10/2024] [Accepted: 09/22/2024] [Indexed: 09/28/2024]
Abstract
BACKGROUND Evidence has established the prominent involvement of rapid eye movement (REM) sleep disturbance in major depressive disorder (MDD). However, the neural correlates of REM sleep in MDD and their clinical significance are less clear. METHODS Cross-sectional and longitudinal polysomnography and resting-state functional MRI data were collected from 131 MDD patients and 71 healthy controls to measure REM sleep and voxel-mirrored homotopic connectivity (VMHC). Correlation and mediation analyses were performed to examine the associations between REM sleep, VMHC, and clinical variables. Moreover, we conducted spatial correlations between the neural correlates of REM sleep and a multimodal collection of reference brain maps to facilitate genetic, structural and functional annotations. RESULTS MDD patients exhibited REM sleep abnormalities manifesting as higher REM sleep latency and lower REM sleep duration, which were correlated with decreased VMHC of the precentral gyrus and inferior parietal lobe and mediated their associations with more severe anxiety symptoms. Longitudinal data showed that VMHC increase of the inferior parietal lobe was related to improvement of depression symptoms in MDD patients. Spatial correlation analyses revealed that the neural correlates of REM sleep in MDD were linked to gene categories primarily involving cellular metabolic process, signal pathway, and ion channel activity as well as linked to cortical microstructure, metabolism, electrophysiology, and cannabinoid receptor. CONCLUSION These findings may add important context to the growing literature on the complex interplay between sleep and MDD, and more broadly may inform future treatment for depression via regulating sleep.
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Affiliation(s)
- Siyu Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei 230032, China
| | - Jingyao Chen
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei 230032, China
| | - Lianzi Guan
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China; Department of Sleep Disorders, Affiliated Psychological Hospital of Anhui Medical University, Hefei 230022, China; Hefei Fourth People's Hospital, Hefei 230022, China; Anhui Mental Health Center, Hefei 230022, China
| | - Li Xu
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China; Department of Sleep Disorders, Affiliated Psychological Hospital of Anhui Medical University, Hefei 230022, China; Hefei Fourth People's Hospital, Hefei 230022, China; Anhui Mental Health Center, Hefei 230022, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei 230032, China
| | - Jie Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei 230032, China
| | - Dao-Min Zhu
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China; Department of Sleep Disorders, Affiliated Psychological Hospital of Anhui Medical University, Hefei 230022, China; Hefei Fourth People's Hospital, Hefei 230022, China; Anhui Mental Health Center, Hefei 230022, China.
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei 230032, China.
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei 230032, China.
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Wang X, Padawer-Curry JA, Bice AR, Kim B, Rosenthal ZP, Lee JM, Goyal MS, Macauley SL, Bauer AQ. Spatiotemporal relationships between neuronal, metabolic, and hemodynamic signals in the awake and anesthetized mouse brain. Cell Rep 2024; 43:114723. [PMID: 39277861 DOI: 10.1016/j.celrep.2024.114723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 07/08/2024] [Accepted: 08/21/2024] [Indexed: 09/17/2024] Open
Abstract
Neurovascular coupling (NVC) and neurometabolic coupling (NMC) provide the basis for functional magnetic resonance imaging and positron emission tomography to map brain neurophysiology. While increases in neuronal activity are often accompanied by increases in blood oxygen delivery and oxidative metabolism, these observations are not the rule. This decoupling is important when interpreting brain network organization (e.g., resting-state functional connectivity [RSFC]) because it is unclear whether changes in NMC/NVC affect RSFC measures. We leverage wide-field optical imaging in Thy1-jRGECO1a mice to map cortical calcium activity in pyramidal neurons, flavoprotein autofluorescence (representing oxidative metabolism), and hemodynamic activity during wake and ketamine/xylazine anesthesia. Spontaneous dynamics of all contrasts exhibit patterns consistent with RSFC. NMC/NVC relative to excitatory activity varies over the cortex. Ketamine/xylazine profoundly alters NVC but not NMC. Compared to awake RSFC, ketamine/xylazine affects metabolic-based connectomes moreso than hemodynamic-based measures of RSFC. Anesthesia-related differences in NMC/NVC timing do not appreciably alter RSFC structure.
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Affiliation(s)
- Xiaodan Wang
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University in Saint Louis, St. Louis, MO 63130, USA
| | - Jonah A Padawer-Curry
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Imaging Sciences Program, Washington University in Saint Louis, St. Louis, MO 63130, USA
| | - Annie R Bice
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Byungchan Kim
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Zachary P Rosenthal
- Department of Psychiatry, University of Pennsylvania Health System Penn Medicine, Philadelphia, PA 19104, USA
| | - Jin-Moo Lee
- Department of Biomedical Engineering, Washington University in Saint Louis, St. Louis, MO 63130, USA; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Manu S Goyal
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Shannon L Macauley
- Department of Physiology, University of Kentucky, Lexington, KY 40508, USA
| | - Adam Q Bauer
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University in Saint Louis, St. Louis, MO 63130, USA; Imaging Sciences Program, Washington University in Saint Louis, St. Louis, MO 63130, USA.
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3
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Blazey T, Lee JJ, Snyder AZ, Goyal MS, Hershey T, Arbeláez AM, Raichle ME. Hyperglycemia selectively increases cerebral non-oxidative glucose consumption without affecting blood flow. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.05.611035. [PMID: 39314314 PMCID: PMC11418958 DOI: 10.1101/2024.09.05.611035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Multiple studies have shown that hyperglycemia increases the cerebral metabolic rate of glucose (CMRglc) in subcortical white matter. This observation remains unexplained. Using positron emission tomography (PET) and euinsulinaemic glucose clamps, we found, for the first time, that acute hyperglycemia increases non-oxidative CMRglc (i.e., aerobic glycolysis (AG)) in subcortical white mater as well as in medial temporal lobe structures, cerebellum and brainstem, all areas with low euglycemic CMRglc. Surprisingly, hyperglycemia did not change regional cerebral blood flow (CBF), the cerebral metabolic rate of oxygen (CMRO2), or the blood-oxygen-level-dependent (BOLD) response. Regional gene expression data reveal that brain regions where CMRglc increased have greater expression of hexokinase 2 (HK2). Simulations of glucose transport revealed that, unlike hexokinase 1, HK2 is not saturated at euglycemia, thus accommodating increased AG during hyperglycemia.
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Affiliation(s)
- Tyler Blazey
- Mallinckrodt Institute of Radiology, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - John J Lee
- Mallinckrodt Institute of Radiology, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Abraham Z Snyder
- Mallinckrodt Institute of Radiology, School of Medicine, Washington University, St. Louis, MO 63110, USA
- Department of Neurology, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Manu S Goyal
- Mallinckrodt Institute of Radiology, School of Medicine, Washington University, St. Louis, MO 63110, USA
- Department of Neurology, School of Medicine, Washington University, St. Louis, MO 63110, USA
- Department of Neuroscience, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Tamara Hershey
- Mallinckrodt Institute of Radiology, School of Medicine, Washington University, St. Louis, MO 63110, USA
- Department of Neurology, School of Medicine, Washington University, St. Louis, MO 63110, USA
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Ana Maria Arbeláez
- Department of Pediatrics, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Marcus E Raichle
- Mallinckrodt Institute of Radiology, School of Medicine, Washington University, St. Louis, MO 63110, USA
- Department of Neurology, School of Medicine, Washington University, St. Louis, MO 63110, USA
- Department of Neuroscience, School of Medicine, Washington University, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University, St. Louis, MO 63105, USA
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Caldwell HG, Hoiland RL, Bain AR, Howe CA, Carr JMJR, Gibbons TD, Durrer CG, Tymko MM, Stacey BS, Bailey DM, Sekhon MS, MacLeod DB, Ainslie PN. Evidence for direct CO 2 -mediated alterations in cerebral oxidative metabolism in humans. Acta Physiol (Oxf) 2024; 240:e14197. [PMID: 38958262 DOI: 10.1111/apha.14197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 06/18/2024] [Accepted: 06/20/2024] [Indexed: 07/04/2024]
Abstract
AIM How the cerebral metabolic rates of oxygen and glucose utilization (CMRO2 and CMRGlc, respectively) are affected by alterations in arterial PCO2 (PaCO2) is equivocal and therefore was the primary question of this study. METHODS This retrospective analysis involved pooled data from four separate studies, involving 41 healthy adults (35 males/6 females). Participants completed stepwise steady-state alterations in PaCO2 ranging between 30 and 60 mmHg. The CMRO2 and CMRGlc were assessed via the Fick approach (CBF × arterial-internal jugular venous difference of oxygen or glucose content, respectively) utilizing duplex ultrasound of the internal carotid artery and vertebral artery to calculate cerebral blood flow (CBF). RESULTS The CMRO2 was altered by 0.5 mL × min-1 (95% CI: -0.6 to -0.3) per mmHg change in PaCO2 (p < 0.001) which corresponded to a 9.8% (95% CI: -13.2 to -6.5) change in CMRO2 with a 9 mmHg change in PaCO2 (inclusive of hypo- and hypercapnia). The CMRGlc was reduced by 7.7% (95% CI: -15.4 to -0.08, p = 0.045; i.e., reduction in net glucose uptake) and the oxidative glucose index (ratio of oxygen to glucose uptake) was reduced by 5.6% (95% CI: -11.2 to 0.06, p = 0.049) with a + 9 mmHg increase in PaCO2. CONCLUSION Collectively, the CMRO2 is altered by approximately 1% per mmHg change in PaCO2. Further, glucose is incompletely oxidized during hypercapnia, indicating reductions in CMRO2 are either met by compensatory increases in nonoxidative glucose metabolism or explained by a reduction in total energy production.
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Affiliation(s)
- Hannah G Caldwell
- Centre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
| | - Ryan L Hoiland
- Centre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
- Department of Anesthesiology, Pharmacology and Therapeutics, Vancouver General Hospital, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, British Columbia, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, British Columbia, Canada
- Collaborative Entity for REsearching Brain Ischemia (CEREBRI), University of British Columbia, Vancouver, British Columbia, Canada
| | - Anthony R Bain
- Department of Kinesiology, Faculty of Human Kinetics, University of Windsor, Windsor, Ontario, Canada
| | - Connor A Howe
- Centre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
| | - Jay M J R Carr
- Centre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
| | - Travis D Gibbons
- Centre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
| | - Cody G Durrer
- Centre for Physical Activity Research, Rigshospitalet, Copenhagen, Denmark
| | - Michael M Tymko
- Division of Critical Care Medicine, Department of Medicine, Faculty of Medicine, Vancouver General Hospital, University of British Columbia, Vancouver, British Columbia, Canada
- Human Cerebrovascular Physiology Laboratory, Department of Human Health and Nutritional Sciences, College of Biological Science, University of Guelph, Guelph, Ontario, Canada
| | - Benjamin S Stacey
- Neurovascular Research Laboratory, Faculty of Life Sciences and Education, University of South Wales, Pontypridd, UK
| | - Damian M Bailey
- Neurovascular Research Laboratory, Faculty of Life Sciences and Education, University of South Wales, Pontypridd, UK
| | - Mypinder S Sekhon
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, British Columbia, Canada
- Collaborative Entity for REsearching Brain Ischemia (CEREBRI), University of British Columbia, Vancouver, British Columbia, Canada
- Division of Critical Care Medicine, Department of Medicine, Faculty of Medicine, Vancouver General Hospital, University of British Columbia, Vancouver, British Columbia, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - David B MacLeod
- Human Pharmacology and Physiology Lab, Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Philip N Ainslie
- Centre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
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Luppi AI, Singleton SP, Hansen JY, Jamison KW, Bzdok D, Kuceyeski A, Betzel RF, Misic B. Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies. Nat Biomed Eng 2024; 8:1142-1161. [PMID: 39103509 PMCID: PMC11410673 DOI: 10.1038/s41551-024-01242-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/02/2024] [Indexed: 08/07/2024]
Abstract
The mechanisms linking the brain's network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveraging principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.
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Affiliation(s)
- Andrea I Luppi
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
| | - S Parker Singleton
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Justine Y Hansen
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Keith W Jamison
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Danilo Bzdok
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- MILA, Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Richard F Betzel
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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Jin S, Wang J, He Y. The brain network hub degeneration in Alzheimer's disease. BIOPHYSICS REPORTS 2024; 10:213-229. [PMID: 39281195 PMCID: PMC11399886 DOI: 10.52601/bpr.2024.230025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/26/2024] [Indexed: 09/18/2024] Open
Abstract
Alzheimer's disease (AD) has been conceptualized as a syndrome of brain network dysfunction. Recent imaging connectomics studies have provided unprecedented opportunities to map structural and functional brain networks in AD. By reviewing molecular, imaging, and computational modeling studies, we have shown that highly connected brain hubs are primarily distributed in the medial and lateral prefrontal, parietal, and temporal regions in healthy individuals and that the hubs are selectively and severely affected in AD as manifested by increased amyloid-beta deposition and regional atrophy, hypo-metabolism, and connectivity dysfunction. Furthermore, AD-related hub degeneration depends on the imaging modality with the most notable degeneration in the medial temporal hubs for morphological covariance networks, the prefrontal hubs for structural white matter networks, and in the medial parietal hubs for functional networks. Finally, the AD-related hub degeneration shows metabolic, molecular, and genetic correlates. Collectively, we conclude that the brain-network-hub-degeneration framework is promising to elucidate the biological mechanisms of network dysfunction in AD, which provides valuable information on potential diagnostic biomarkers and promising therapeutic targets for the disease.
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Affiliation(s)
- Suhui Jin
- Institute for Brain Research and Rehabilitation, Guangzhou 510631, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, Guangzhou 510631, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Guangzhou 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou 510631, China
| | - Yong He
- IDG/McGovern Institute for Brain Research, Beijing 100875, China
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
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7
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Clairis N, Barakat A, Brochard J, Xin L, Sandi C. A neurometabolic mechanism involving dmPFC/dACC lactate in physical effort-based decision-making. Mol Psychiatry 2024:10.1038/s41380-024-02726-y. [PMID: 39215184 DOI: 10.1038/s41380-024-02726-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 08/21/2024] [Accepted: 08/23/2024] [Indexed: 09/04/2024]
Abstract
Motivation levels vary across individuals, yet the underlying mechanisms driving these differences remain elusive. The dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC) and the anterior insula (aIns) play crucial roles in effort-based decision-making. Here, we investigate the influence of lactate, a key metabolite involved in energy metabolism and signaling, on decisions involving both physical and mental effort, as well as its effects on neural activation. Using proton magnetic resonance spectroscopy and functional MRI in 63 participants, we find that higher lactate levels in the dmPFC/dACC are associated with reduced motivation for physical effort, a relationship mediated by neural activity within this region. Additionally, plasma and dmPFC/dACC lactate levels correlate, suggesting a systemic influence on brain metabolism. Supported by path analysis, our results highlight lactate's role as a modulator of dmPFC/dACC activity, hinting at a neurometabolic mechanism that integrates both peripheral and central metabolic states with brain function in effort-based decision-making.
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Affiliation(s)
- Nicolas Clairis
- Laboratory of Behavioral Genetics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Arthur Barakat
- Laboratory of Behavioral Genetics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jules Brochard
- Transdisciplinary Research Areas, Life and Health, University of Bonn, Bonn, Germany
| | - Lijing Xin
- Center for Biomedical Imaging (CIBM), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Institute of Physics (IPHYS), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Carmen Sandi
- Laboratory of Behavioral Genetics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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Dobolyi A, Cservenák M, Bagó AG, Chen C, Stepanova A, Paal K, Lee J, Palkovits M, Hudson G, Chinopoulos C. Cell-specific expression of key mitochondrial enzymes limits OXPHOS in astrocytes of the adult human neocortex and hippocampal formation. Commun Biol 2024; 7:1045. [PMID: 39181993 PMCID: PMC11344819 DOI: 10.1038/s42003-024-06751-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 08/16/2024] [Indexed: 08/27/2024] Open
Abstract
The astrocyte-to-neuron lactate shuttle model entails that, upon glutamatergic neurotransmission, glycolytically derived pyruvate in astrocytes is mainly converted to lactate instead of being entirely catabolized in mitochondria. The mechanism of this metabolic rewiring and its occurrence in human brain are unclear. Here by using immunohistochemistry (4 brains) and imaging mass cytometry (8 brains) we show that astrocytes of the adult human neocortex and hippocampal formation express barely detectable amounts of mitochondrial proteins critical for performing oxidative phosphorylation (OXPHOS). These data are corroborated by queries of transcriptomes (107 brains) of neuronal versus non-neuronal cells fetched from the Allen Institute for Brain Science for genes coding for a much larger repertoire of entities contributing to OXPHOS, showing that human non-neuronal elements barely expressed mRNAs coding for such proteins. With less OXPHOS, human brain astrocytes are thus bound to produce more lactate to avoid interruption of glycolysis.
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Affiliation(s)
- Arpád Dobolyi
- Laboratory of Neuromorphology, Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary
- Laboratory of Molecular and Systems Neurobiology, Department of Physiology and Neurobiology, Eotvos Lorand University, Budapest, Hungary
| | - Melinda Cservenák
- Laboratory of Molecular and Systems Neurobiology, Department of Physiology and Neurobiology, Eotvos Lorand University, Budapest, Hungary
| | - Attila G Bagó
- National Institute of Mental Health, Neurology and Neurosurgery, Department of Surgical Neurooncology, Budapest, Hungary
| | - Chun Chen
- Wellcome Centre for Mitochondrial Research, Bioscience Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Anna Stepanova
- Brain and Mind Research Institute, Weill Cornell Medical College, New York, NY, USA
| | - Krisztina Paal
- Institute of Biochemistry and Molecular Biology, Department of Biochemistry, Semmelweis University, Budapest, Hungary
| | - Jeonghyoun Lee
- Institute of Biochemistry and Molecular Biology, Department of Biochemistry, Semmelweis University, Budapest, Hungary
| | - Miklós Palkovits
- Laboratory of Neuromorphology, Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary
- Human Brain Tissue Bank, Semmelweis University, Budapest, Hungary
| | - Gavin Hudson
- Wellcome Centre for Mitochondrial Research, Bioscience Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Christos Chinopoulos
- Institute of Biochemistry and Molecular Biology, Department of Biochemistry, Semmelweis University, Budapest, Hungary.
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9
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Schleifer CH, Chang SE, Amir CM, O'Hora KP, Fung H, Kang JWD, Kushan-Wells L, Daly E, Di Fabio F, Frascarelli M, Gudbrandsen M, Kates WR, Murphy D, Addington J, Anticevic A, Cadenhead KS, Cannon TD, Cornblatt BA, Keshavan M, Mathalon DH, Perkins DO, Stone W, Walker E, Woods SW, Uddin LQ, Kumar K, Hoftman GD, Bearden CE. Unique functional neuroimaging signatures of genetic versus clinical high risk for psychosis. Biol Psychiatry 2024:S0006-3223(24)01538-5. [PMID: 39181389 DOI: 10.1016/j.biopsych.2024.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 08/05/2024] [Accepted: 08/08/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND 22q11.2 Deletion Syndrome (22qDel) is a copy number variant (CNV) associated with psychosis and other neurodevelopmental disorders. Adolescents at clinical high risk for psychosis (CHR) are identified based on the presence of subthreshold psychosis symptoms. Whether common neural substrates underlie these distinct high-risk populations is unknown. We compared functional brain measures in 22qDel and CHR cohorts and mapped results to biological pathways. METHODS We analyzed two large multi-site cohorts with resting-state functional MRI (rs-fMRI): 1) 22qDel (n=164, 47% female) and typically developing (TD) controls (n=134, 56% female); 2) CHR individuals (n=244, 41% female) and TD controls (n=151, 46% female) from the North American Prodrome Longitudinal Study-2. We computed global brain connectivity (GBC), local connectivity (LC), and brain signal variability (BSV) across cortical regions, testing case-control differences for 22qDel and CHR separately. Group difference maps were related to published brain maps using autocorrelation-preserving permutation. RESULTS BSV, LC, and GBC are significantly disrupted in 22qDel compared with TD controls (False Discovery Rate q<0.05). Spatial maps of BSV and LC differences are highly correlated with each other, unlike GBC. In CHR, only LC is significantly altered versus controls, with a different spatial pattern compared to 22qDel. Group differences map onto biological gradients, with 22qDel effects strongest in regions with high predicted blood flow and metabolism. CONCLUSION 22qDel and CHR exhibit divergent effects on fMRI temporal variability and multi-scale functional connectivity. In 22qDel, strong and convergent disruptions in BSV and LC not seen in CHR individuals suggest distinct functional brain alterations.
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Affiliation(s)
- Charles H Schleifer
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Sarah E Chang
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Carolyn M Amir
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Kathleen P O'Hora
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Hoki Fung
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Jee Won D Kang
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Leila Kushan-Wells
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Eileen Daly
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, UK
| | - Fabio Di Fabio
- Department of Human Neurosciences, Sapienza University, Rome, Italy
| | | | - Maria Gudbrandsen
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, UK; Centre for Psychological Research (CREW), School of Psychology, University of Roehampton, London, UK
| | - Wendy R Kates
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Declan Murphy
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, UK
| | - Jean Addington
- Department of Psychiatry, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Alan Anticevic
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | | | - Tyrone D Cannon
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - Barbara A Cornblatt
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Matcheri Keshavan
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California, and San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - William Stone
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Elaine Walker
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Scott W Woods
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Kuldeep Kumar
- Centre de Recherche du CHU Sainte-Justine, University of Montreal, Montreal, Canada
| | - Gil D Hoftman
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA; Department of Psychology, University of California, Los Angeles, CA, USA.
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10
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Ouyang M, Detre JA, Hyland JL, Sindabizera KL, Kuschner ES, Edgar JC, Peng Y, Huang H. Spatiotemporal cerebral blood flow dynamics underlies emergence of the limbic-sensorimotor-association cortical gradient in human infancy. RESEARCH SQUARE 2024:rs.3.rs-4761517. [PMID: 39149463 PMCID: PMC11326418 DOI: 10.21203/rs.3.rs-4761517/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Infant cerebral blood flow (CBF) delivers nutrients and oxygen to fulfill brain energy consumption requirements for the fastest period of postnatal brain development across the lifespan. However, organizing principle of whole-brain CBF dynamics during infancy remains obscure. Leveraging a unique cohort of 100+ infants with high-resolution arterial spin labeled MRI, we found the emergence of the cortical hierarchy revealed by the highest-resolution infant CBF maps available to date. Infant CBF across cortical regions increased in a biphasic pattern with initial rapid and sequentially slower rate, with break-point ages increasing along the limbic-sensorimotor-association cortical gradient. Increases in CBF in sensorimotor cortices were associated with enhanced language and motor skills, and frontoparietal association cortices for cognitive skills. The study discovered emergence of the hierarchical limbic-sensorimotor-association cortical gradient in infancy, and offers standardized reference of infant brain CBF and insight into the physiological basis of cortical specialization and real-world infant developmental functioning.
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Affiliation(s)
- Minhui Ouyang
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, United States
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - John A. Detre
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Jessica L. Hyland
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, United States
| | - Kay L. Sindabizera
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, United States
| | - Emily S Kuschner
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, United States
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - J. Christopher Edgar
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, United States
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Yun Peng
- Department of Radiology, Beijing Children’s Hospital, Capital Medical University, Beijing, 100045, China
| | - Hao Huang
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, United States
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
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11
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Kapogiannis D, Manolopoulos A, Mullins R, Avgerinos K, Delgado-Peraza F, Mustapic M, Nogueras-Ortiz C, Yao PJ, Pucha KA, Brooks J, Chen Q, Haas SS, Ge R, Hartnell LM, Cookson MR, Egan JM, Frangou S, Mattson MP. Brain responses to intermittent fasting and the healthy living diet in older adults. Cell Metab 2024; 36:1668-1678.e5. [PMID: 38901423 PMCID: PMC11305918 DOI: 10.1016/j.cmet.2024.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/29/2024] [Accepted: 05/29/2024] [Indexed: 06/22/2024]
Abstract
Diet may promote brain health in metabolically impaired older individuals. In an 8-week randomized clinical trial involving 40 cognitively intact older adults with insulin resistance, we examined the effects of 5:2 intermittent fasting and the healthy living diet on brain health. Although intermittent fasting induced greater weight loss, the two diets had comparable effects in improving insulin signaling biomarkers in neuron-derived extracellular vesicles, decreasing the brain-age-gap estimate (reflecting the pace of biological aging of the brain) on magnetic resonance imaging, reducing brain glucose on magnetic resonance spectroscopy, and improving blood biomarkers of carbohydrate and lipid metabolism, with minimal changes in cerebrospinal fluid biomarkers for Alzheimer's disease. Intermittent fasting and healthy living improved executive function and memory, with intermittent fasting benefiting more certain cognitive measures. In exploratory analyses, sex, body mass index, and apolipoprotein E and SLC16A7 genotypes modulated diet effects. The study provides a blueprint for assessing brain effects of dietary interventions and motivates further research on intermittent fasting and continuous diets for brain health optimization. For further information, please see ClinicalTrials.gov registration: NCT02460783.
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Affiliation(s)
- Dimitrios Kapogiannis
- Intramural Research Program, Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA.
| | - Apostolos Manolopoulos
- Intramural Research Program, Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, USA
| | - Roger Mullins
- Morgan State University, Core Lab, Baltimore, MD, USA
| | | | - Francheska Delgado-Peraza
- Intramural Research Program, Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, USA
| | - Maja Mustapic
- Intramural Research Program, Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, USA
| | - Carlos Nogueras-Ortiz
- Intramural Research Program, Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, USA
| | - Pamela J Yao
- Intramural Research Program, Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, USA
| | - Krishna A Pucha
- Intramural Research Program, Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, USA
| | - Janet Brooks
- Intramural Research Program, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Qinghua Chen
- Intramural Research Program, Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, USA
| | - Shalaila S Haas
- Mt. Sinai School of Medicine, Department of Psychiatry, New York, NY, USA
| | - Ruiyang Ge
- Center for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Lisa M Hartnell
- Intramural Research Program, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Mark R Cookson
- Intramural Research Program, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Josephine M Egan
- Intramural Research Program, Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, USA
| | - Sophia Frangou
- Mt. Sinai School of Medicine, Department of Psychiatry, New York, NY, USA; Center for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Mark P Mattson
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA
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12
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Markett S, Boeken OJ, Wudarczyk OA. Multimodal imaging investigation of structural rich club alterations in Alzheimer's disease and mild cognitive impairment: Amyloid deposition, structural atrophy, and functional activation differences. Eur J Neurosci 2024; 60:4169-4181. [PMID: 38779858 DOI: 10.1111/ejn.16384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 04/03/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024]
Abstract
Alzheimer's disease (AD) is characterized by significant cerebral dysfunction, including increased amyloid deposition, gray matter atrophy, and changes in brain function. The involvement of highly connected network hubs, known as the "rich club," in the pathology of the disease remains inconclusive despite previous research efforts. In this study, we aimed to systematically assess the link between the rich club and AD using a multimodal neuroimaging approach. We employed network analyses of diffusion magnetic resonance imaging (MRI), longitudinal assessments of gray matter atrophy, amyloid deposition measurements using positron emission tomography (PET) imaging, and meta-analytic data on functional activation differences. Our study focused on evaluating the role of both the structural brain network's core and extended rich club regions in individuals with mild cognitive impairment (MCI) and those diagnosed with AD. Our findings revealed that structural rich club regions exhibited accelerated gray matter atrophy and increased amyloid deposition in both MCI and AD. Importantly, these regions remained unaffected by altered functional activation patterns observed outside the core rich club regions. These results shed light on the connection between two major AD biomarkers and the rich club, providing valuable insights into AD as a potential disconnection syndrome.
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Affiliation(s)
| | - Ole J Boeken
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin, Berlin, Germany
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13
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Volpi T, Silvestri E, Aiello M, Lee JJ, Vlassenko AG, Goyal MS, Corbetta M, Bertoldo A. The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity? J Cereb Blood Flow Metab 2024; 44:1433-1449. [PMID: 38443762 PMCID: PMC11342718 DOI: 10.1177/0271678x241237974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 01/05/2024] [Accepted: 02/11/2024] [Indexed: 03/07/2024]
Abstract
Brain glucose metabolism, which can be investigated at the macroscale level with [18F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain's spontaneous activity can describe local metabolism is unknown. Here, using two multimodal datasets of healthy participants, we built a multivariable multilevel model of functional-metabolic associations, assessing multiple functional features, describing the 1) rs-fMRI signal, 2) hemodynamic response, 3) static and 4) time-varying functional connectivity, as predictors of the human brain's metabolic architecture. The full model was trained on one dataset and tested on the other to assess its reproducibility. We found that functional-metabolic spatial coupling is nonlinear and heterogeneous across the brain, and that local measures of rs-fMRI activity and synchrony are more tightly coupled to local metabolism. In the testing dataset, the degree of functional-metabolic spatial coupling was also related to peripheral metabolism. Overall, although a significant proportion of regional metabolic variability can be described by measures of spontaneous activity, additional efforts are needed to explain the remaining variance in the brain's 'dark energy'.
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Affiliation(s)
- Tommaso Volpi
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Erica Silvestri
- Department of Information Engineering, University of Padova, Padova, Italy
| | | | - John J Lee
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Andrei G Vlassenko
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Manu S Goyal
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Maurizio Corbetta
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Neuroscience, University of Padova, Padova, Italy
| | - Alessandra Bertoldo
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Information Engineering, University of Padova, Padova, Italy
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14
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Herbet G, Duffau H, Mandonnet E. Predictors of cognition after glioma surgery: connectotomy, structure-function phenotype, plasticity. Brain 2024; 147:2621-2635. [PMID: 38573324 DOI: 10.1093/brain/awae093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 02/19/2024] [Accepted: 03/09/2024] [Indexed: 04/05/2024] Open
Abstract
Determining preoperatively the maximal extent of resection that would preserve cognitive functions is the core challenge of brain tumour surgery. Over the past decade, the methodological framework to achieve this goal has been thoroughly renewed: the population-level topographically-focused voxel-based lesion-symptom mapping has been progressively overshadowed by machine learning (ML) algorithmics, in which the problem is framed as predicting cognitive outcomes in a patient-specific manner from a typically large set of variables. However, the choice of these predictors is of utmost importance, as they should be both informative and parsimonious. In this perspective, we first introduce the concept of connectotomy: instead of parameterizing resection topography through the status (intact/resected) of a huge number of voxels (or parcels) paving the whole brain in the Cartesian 3D-space, the connectotomy models the resection in the connectivity space, by computing a handful number of networks disconnection indices, measuring how the structural connectivity sustaining each network of interest was hit by the resection. This connectivity-informed reduction of dimensionality is a necessary step for efficiently implementing ML tools, given the relatively small number of patient-examples in available training datasets. We further argue that two other major sources of interindividual variability must be considered to improve the accuracy with which outcomes are predicted: the underlying structure-function phenotype and neuroplasticity, for which we provide an in-depth review and propose new ways of determining relevant predictors. We finally discuss the benefits of our approach for precision surgery of glioma.
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Affiliation(s)
- Guillaume Herbet
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier 34090, France
- Praxiling lab, UMR5267 CNRS & Paul Valéry University, Montpellier 34090, France
- Department of Medicine, University of Montpellier, Montpellier 34090, France
- Institut Universitaire de France, Paris 75000, France
| | - Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier 34090, France
- Department of Medicine, University of Montpellier, Montpellier 34090, France
- Team 'Plasticity of Central Nervous System, Stem Cells and Glial Tumors', U1191 Laboratory, Institute of Functional Genomics, National Institute for Health and Medical Research (INSERM), University of Montpellier, Montpellier 34000, France
| | - Emmanuel Mandonnet
- Department of Neurosurgery, Lariboisière Hospital, AP-HP, Paris 75010, France
- Frontlab, CNRS UMR 7225, INSERM U1127, Paris Brain Institute (ICM), Paris 75013, France
- Université de Paris Cité, UFR de médecine, Paris 75005, France
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15
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Genc S, Ball G, Chamberland M, Raven EP, Tax CM, Ward I, Yang JYM, Palombo M, Jones DK. MRI signatures of cortical microstructure in human development align with oligodendrocyte cell-type expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.30.605934. [PMID: 39131383 PMCID: PMC11312524 DOI: 10.1101/2024.07.30.605934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Neuroanatomical changes to the cortex during adolescence have been well documented using MRI, revealing ongoing cortical thinning and volume loss with age. However, the underlying cellular mechanisms remain elusive with conventional neuroimaging. Recent advances in MRI hardware and new biophysical models of tissue informed by diffusion MRI data hold promise for identifying the cellular changes driving these morphological observations. This study used ultra-strong gradient MRI to obtain high-resolution, in vivo estimates of cortical neurite and soma microstructure in sample of typically developing children and adolescents. Cortical neurite signal fraction, attributed to neuronal and glial processes, increased with age (mean R2 fneurite=.53, p<3.3e-11, 11.91% increase over age), while apparent soma radius decreased (mean R2 Rsoma=.48, p<4.4e-10, 1% decrease over age) across domain-specific networks. To complement these findings, developmental patterns of cortical gene expression in two independent post-mortem databases were analysed. This revealed increased expression of genes expressed in oligodendrocytes, and excitatory neurons, alongside a relative decrease in expression of genes expressed in astrocyte, microglia and endothelial cell-types. Age-related genes were significantly enriched in cortical oligodendrocytes, oligodendrocyte progenitors and Layer 5-6 neurons (pFDR<.001) and prominently expressed in adolescence and young adulthood. The spatial and temporal alignment of oligodendrocyte cell-type gene expression with neurite and soma microstructural changes suggest that ongoing cortical myelination processes contribute to adolescent cortical development. These findings highlight the role of intra-cortical myelination in cortical maturation during adolescence and into adulthood.
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Affiliation(s)
- Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
- Developmental Imaging, Clinical Sciences, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Neuroscience Advanced Clinical Imaging Service (NACIS), Department of Neurosurgery, The Royal Children's Hospital, Parkville, Victoria, Australia
| | - Gareth Ball
- Developmental Imaging, Clinical Sciences, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
- Eindhoven University of Technology, Department of Mathematics and Computer Science, Eindhoven, The Netherlands
| | - Erika P Raven
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, USA
| | - Chantal Mw Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Isobel Ward
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
- Data and Analysis for Social Care and Health, Office for National Statistics, Newport, United Kingdom
| | - Joseph Yuan-Mou Yang
- Developmental Imaging, Clinical Sciences, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Neuroscience Advanced Clinical Imaging Service (NACIS), Department of Neurosurgery, The Royal Children's Hospital, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
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16
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Lu Y, Cui Y, Cao L, Dong Z, Cheng L, Wu W, Wang C, Liu X, Liu Y, Zhang B, Li D, Zhao B, Wang H, Li K, Ma L, Shi W, Li W, Ma Y, Du Z, Zhang J, Xiong H, Luo N, Liu Y, Hou X, Han J, Sun H, Cai T, Peng Q, Feng L, Wang J, Paxinos G, Yang Z, Fan L, Jiang T. Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology. Sci Bull (Beijing) 2024; 69:2241-2259. [PMID: 38580551 DOI: 10.1016/j.scib.2024.03.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/18/2024] [Accepted: 03/11/2024] [Indexed: 04/07/2024]
Abstract
The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.
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Affiliation(s)
- Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Long Cao
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China; Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhenwei Dong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Luqi Cheng
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Wen Wu
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Changshuo Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Xinyi Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Youtong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Deying Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bokai Zhao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yawei Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Zongchang Du
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaqi Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hui Xiong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yanyan Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Xiaoxiao Hou
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jinglu Han
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Hongji Sun
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tao Cai
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Qiang Peng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Linqing Feng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - George Paxinos
- Neuroscience Research Australia and The University of New South Wales, Sydney NSW 2031, Australia
| | - Zhengyi Yang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China.
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China.
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17
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Zhong YL, Hu RY, Huang X. Aberrant Neurovascular Coupling in Diabetic Retinopathy Using Arterial Spin Labeling (ASL) and Functional Magnetic Resonance Imaging (fMRI) methods. Diabetes Metab Syndr Obes 2024; 17:2809-2822. [PMID: 39081370 PMCID: PMC11288319 DOI: 10.2147/dmso.s465103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 07/07/2024] [Indexed: 08/02/2024] Open
Abstract
Background Previous imaging studies have demonstrated that diabetic retinopathy (DR) is linked to structural and functional abnormalities in the brain. However, the extent to which DR patients exhibit abnormal neurovascular coupling remains largely unknown. Methods Thirty-one patients with DR and 31 sex- and age-matched healthy controls underwent resting-state functional magnetic resonance imaging (rs-fMRI) to calculate functional connectivity strength (FCS) and arterial spin-labeling imaging (ASL) to calculate cerebral blood flow (CBF). The study compared CBF-FCS coupling across the entire grey matter and CBF/FCS ratios (representing blood supply per unit of connectivity strength) per voxel between the two groups. Additionally, a support vector machine (SVM) method was employed to differentiate between diabetic retinopathy (DR) patients and healthy controls (HC). Results In DRpatients compared to healthy controls, there was a reduction in CBF-FCS coupling across the entire grey matter. Specifically, DR patients exhibited elevated CBF/FCS ratios primarily in the primary visual cortex, including the right calcarine fissure and surrounding cortex. On the other hand, reduced CBF/FCS ratios were mainly observed in premotor and supplementary motor areas, including the left middle frontal gyrus. Conclusion An elevated CBF/FCS ratio suggests that patients with DR may have a reduced volume of gray matter in the brain. A decrease in its ratio indicates a decrease in regional CBF in patients with DR. These findings suggest that neurovascular decoupling in the visual cortex, as well as in the supplementary motor and frontal gyrus, may represent a neuropathological mechanism in diabetic retinopathy.
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Affiliation(s)
- Yu-Lin Zhong
- Department of Ophthalmology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, 330006, People’s Republic of China
| | - Rui-Yang Hu
- School of Ophthalmology and Optometry, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, People’s Republic of China
| | - Xin Huang
- Department of Ophthalmology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, 330006, People’s Republic of China
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18
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Klug S, Murgaš M, Godbersen GM, Hacker M, Lanzenberger R, Hahn A. Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain. Neuroimage 2024; 295:120658. [PMID: 38810891 DOI: 10.1016/j.neuroimage.2024.120658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 04/22/2024] [Accepted: 05/27/2024] [Indexed: 05/31/2024] Open
Abstract
PURPOSE The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach. METHODS 51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions. RESULTS Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning. CONCLUSIONS Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.
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Affiliation(s)
- Sebastian Klug
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Godber M Godbersen
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria.
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19
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Granov R, Vedad S, Wang SH, Durham A, Shah D, Pasinetti GM. The Role of the Neural Exposome as a Novel Strategy to Identify and Mitigate Health Inequities in Alzheimer's Disease and Related Dementias. Mol Neurobiol 2024:10.1007/s12035-024-04339-6. [PMID: 38967905 DOI: 10.1007/s12035-024-04339-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 06/28/2024] [Indexed: 07/06/2024]
Abstract
With the continuous increase of the elderly population, there is an urgency to understand and develop relevant treatments for Alzheimer's disease and related dementias (ADRD). In tandem with this, the prevalence of health inequities continues to rise as disadvantaged communities fail to be included in mainstream research. The neural exposome poses as a relevant mechanistic approach and tool for investigating ADRD onset, progression, and pathology as it accounts for several different factors: exogenous, endogenous, and behavioral. Consequently, through the neural exposome, health inequities can be addressed in ADRD research. In this paper, we address how the neural exposome relates to ADRD by contributing to the discourse through defining how the neural exposome can be developed as a tool in accordance with machine learning. Through this, machine learning can allow for developing a greater insight into the application of transferring and making sense of experimental mouse models exposed to health inequities and potentially relate it to humans. The overall goal moving beyond this paper is to define a multitude of potential factors that can increase the risk of ADRD onset and integrate them to create an interdisciplinary approach to the study of ADRD and subsequently translate the findings to clinical research.
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Affiliation(s)
- Ravid Granov
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
| | - Skyler Vedad
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
| | - Shu-Han Wang
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
| | - Andrea Durham
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
| | - Divyash Shah
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
| | - Giulio Maria Pasinetti
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA.
- Geriatrics Research, Education and Clinical Center, JJ Peters VA Medical Center, Bronx, NY, 10468, USA.
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20
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Saberi A, Wischnewski KJ, Jung K, Lotter LD, Schaare HL, Banaschewski T, Barker GJ, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Lemaitre H, Poustka L, Hohmann S, Holz N, Baeuchl C, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Paus T, Dukart J, Bernhardt BC, Popovych OV, Eickhoff SB, Valk SL. Adolescent maturation of cortical excitation-inhibition balance based on individualized biophysical network modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.18.599509. [PMID: 38948771 PMCID: PMC11213014 DOI: 10.1101/2024.06.18.599509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
The balance of excitation and inhibition is a key functional property of cortical microcircuits which changes through the lifespan. Adolescence is considered a crucial period for the maturation of excitation-inhibition balance. This has been primarily observed in animal studies, yet human in vivo evidence on adolescent maturation of the excitation-inhibition balance at the individual level is limited. Here, we developed an individualized in vivo marker of regional excitation-inhibition balance in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional magnetic resonance imaging data from two independent cross-sectional (N = 752) and longitudinal (N = 149) cohorts. We found a widespread relative increase of inhibition in association cortices paralleled by a relative age-related increase of excitation, or lack of change, in sensorimotor areas across both datasets. This developmental pattern co-aligned with multiscale markers of sensorimotor-association differentiation. The spatial pattern of excitation-inhibition development in adolescence was robust to inter-individual variability of structural connectomes and modeling configurations. Notably, we found that alternative simulation-based markers of excitation-inhibition balance show a variable sensitivity to maturational change. Taken together, our study highlights an increase of inhibition during adolescence in association areas using cross sectional and longitudinal data, and provides a robust computational framework to estimate microcircuit maturation in vivo at the individual level.
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Affiliation(s)
- Amin Saberi
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Kevin J Wischnewski
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Mathematics, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Dusseldorf, Germany
| | - Kyesam Jung
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Leon D Lotter
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Max Planck School of Cognition, Stephanstrasse 1A, 04103 Leipzig, Germany
| | - H Lina Schaare
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Sylvane Desrivières
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, Vermont, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- German Center for Mental Health (DZPG), site Berlin-Potsdam, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli; Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli; Gif-sur-Yvette, France
- AP-HP. Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli; Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | | | - Herve Lemaitre
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
- Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, 33076 Bordeaux, France
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Nathalie Holz
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Christian Baeuchl
- Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Nilakshi Vaidya
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Germany
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Tomáš Paus
- Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
| | - Juergen Dukart
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Oleksandr V Popovych
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sofie L Valk
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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21
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Sydnor VJ, Bagautdinova J, Larsen B, Arcaro MJ, Barch DM, Bassett DS, Alexander-Bloch AF, Cook PA, Covitz S, Franco AR, Gur RE, Gur RC, Mackey AP, Mehta K, Meisler SL, Milham MP, Moore TM, Müller EJ, Roalf DR, Salo T, Schubiner G, Seidlitz J, Shinohara RT, Shine JM, Yeh FC, Cieslak M, Satterthwaite TD. A sensorimotor-association axis of thalamocortical connection development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.13.598749. [PMID: 38915591 PMCID: PMC11195180 DOI: 10.1101/2024.06.13.598749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Human cortical development follows a sensorimotor-to-association sequence during childhood and adolescence1-6. The brain's capacity to enact this sequence over decades indicates that it relies on intrinsic mechanisms to regulate inter-regional differences in the timing of cortical maturation, yet regulators of human developmental chronology are not well understood. Given evidence from animal models that thalamic axons modulate windows of cortical plasticity7-12, here we evaluate the overarching hypothesis that structural connections between the thalamus and cortex help to coordinate cortical maturational heterochronicity during youth. We first introduce, cortically annotate, and anatomically validate a new atlas of human thalamocortical connections using diffusion tractography. By applying this atlas to three independent youth datasets (ages 8-23 years; total N = 2,676), we reproducibly demonstrate that thalamocortical connections develop along a maturational gradient that aligns with the cortex's sensorimotor-association axis. Associative cortical regions with thalamic connections that take longest to mature exhibit protracted expression of neurochemical, structural, and functional markers indicative of higher circuit plasticity as well as heightened environmental sensitivity. This work highlights a central role for the thalamus in the orchestration of hierarchically organized and environmentally sensitive windows of cortical developmental malleability.
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Affiliation(s)
- Valerie J. Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joëlle Bagautdinova
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Michael J. Arcaro
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Deanna M. Barch
- Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
- Department of Psychological & Brain Sciences, Washington University in St Louis, St Louis, Missouri, USA
| | - Dani S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- The Santa Fe Institute, Santa Fe, NM, USA
| | - Aaron F. Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Philip A. Cook
- Penn Image Computing and Science Lab (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, Schools of Engineering and Medicine, Stanford University, Stanford, CA, USA
| | - Alexandre R. Franco
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Strategic Data Initiatives, Child Mind Institute, New York, NY, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | - Raquel E. Gur
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C. Gur
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Brain Behavior Laboratory, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Allyson P. Mackey
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Kahini Mehta
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Steven L. Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Division of Medical Sciences, Cambridge, MA, USA
| | - Michael P. Milham
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Tyler M. Moore
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Brain Behavior Laboratory, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eli J. Müller
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - David R. Roalf
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Brain Behavior Laboratory, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Taylor Salo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jakob Seidlitz
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - James M. Shine
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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22
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Jiang Y, Palaniyappan L, Luo C, Chang X, Zhang J, Tang Y, Zhang T, Li C, Zhou E, Yu X, Li W, An D, Zhou D, Huang CC, Tsai SJ, Lin CP, Cheng J, Wang J, Yao D, Cheng W, Feng J. Neuroimaging epicenters as potential sites of onset of the neuroanatomical pathology in schizophrenia. SCIENCE ADVANCES 2024; 10:eadk6063. [PMID: 38865456 PMCID: PMC11168466 DOI: 10.1126/sciadv.adk6063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 05/08/2024] [Indexed: 06/14/2024]
Abstract
Schizophrenia lacks a clear definition at the neuroanatomical level, capturing the sites of origin and progress of this disorder. Using a network-theory approach called epicenter mapping on cross-sectional magnetic resonance imaging from 1124 individuals with schizophrenia, we identified the most likely "source of origin" of the structural pathology. Our results suggest that the Broca's area and adjacent frontoinsular cortex may be the epicenters of neuroanatomical pathophysiology in schizophrenia. These epicenters can predict an individual's response to treatment for psychosis. In addition, cross-diagnostic similarities based on epicenter mapping over of 4000 individuals diagnosed with neurological, neurodevelopmental, or psychiatric disorders appear to be limited. When present, these similarities are restricted to bipolar disorder, major depressive disorder, and obsessive-compulsive disorder. We provide a comprehensive framework linking schizophrenia-specific epicenters to multiple levels of neurobiology, including cognitive processes, neurotransmitter receptors and transporters, and human brain gene expression. Epicenter mapping may be a reliable tool for identifying the potential onset sites of neural pathophysiology in schizophrenia.
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Affiliation(s)
- Yuchao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, PR China
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montréal, Quebec, Canada
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and Technology, University of Electronic Science and Technology of China, Chengdu, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, PR China
- Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Xiao Chang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, PR China
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, PR China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Enpeng Zhou
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, PR China
| | - Xin Yu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, PR China
| | - Wei Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Dongmei An
- Department of Neurology, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Dong Zhou
- Department of Neurology, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, PR China
- Shanghai Changning Mental Health Center, Shanghai, PR China
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, PR China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and Technology, University of Electronic Science and Technology of China, Chengdu, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, PR China
- Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, PR China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, PR China
- Fudan ISTBI—ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, PR China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, PR China
- Fudan ISTBI—ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, PR China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, PR China
- Zhangjiang Fudan International Innovation Center, Shanghai, PR China
- School of Data Science, Fudan University, Shanghai, PR China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
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23
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Casimir P, Iwata R, Vanderhaeghen P. Linking mitochondria metabolism, developmental timing, and human brain evolution. Curr Opin Genet Dev 2024; 86:102182. [PMID: 38555796 PMCID: PMC11190843 DOI: 10.1016/j.gde.2024.102182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/03/2024] [Accepted: 03/05/2024] [Indexed: 04/02/2024]
Abstract
Changes in developmental timing are an important factor of evolution in organ shape and function. This is particularly striking for human brain development, which, compared with other mammals, is considerably prolonged at the level of the cerebral cortex, resulting in brain neoteny. Here, we review recent findings that indicate that mitochondria and metabolism contribute to species differences in the tempo of cortical neuron development. Mitochondria display species-specific developmental timeline and metabolic activity patterns that are highly correlated with the speed of neuron maturation. Enhancing mitochondrial activity in human cortical neurons results in their accelerated maturation, while its reduction leads to decreased maturation rates in mouse neurons. Together with other global and gene-specific mechanisms, mitochondria thus act as a cellular hourglass of neuronal developmental tempo and may thereby contribute to species-specific features of human brain ontogeny.
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Affiliation(s)
- Pierre Casimir
- VIB Center for Brain & Disease Research, 3000 Leuven, Belgium; Department of Neurosciences, Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium; Université Libre de Bruxelles (ULB), Institut de Recherches en Biologie Humaine et Moléculaire (IRIBHM), and ULB Neuroscience Institute (UNI), 1070 Brussels, Belgium; Department of Neurology, Centre Hospitalier Universitaire Brugmann, ULB, 1020 Brussels, Belgium
| | - Ryohei Iwata
- VIB Center for Brain & Disease Research, 3000 Leuven, Belgium; Department of Neurosciences, Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium. https://twitter.com/@Ryo2Iwata
| | - Pierre Vanderhaeghen
- VIB Center for Brain & Disease Research, 3000 Leuven, Belgium; Department of Neurosciences, Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium.
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24
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Ourry V, Binette AP, St-Onge F, Strikwerda-Brown C, Chagnot A, Poirier J, Breitner J, Arenaza-Urquijo EM, Rabin JS, Buckley R, Gonneaud J, Marchant NL, Villeneuve S. How Do Modifiable Risk Factors Affect Alzheimer's Disease Pathology or Mitigate Its Effect on Clinical Symptom Expression? Biol Psychiatry 2024; 95:1006-1019. [PMID: 37689129 DOI: 10.1016/j.biopsych.2023.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 08/11/2023] [Accepted: 09/03/2023] [Indexed: 09/11/2023]
Abstract
Epidemiological studies show that modifiable risk factors account for approximately 40% of the population variability in risk of developing dementia, including sporadic Alzheimer's disease (AD). Recent findings suggest that these factors may also modify disease trajectories of people with autosomal-dominant AD. With positron emission tomography imaging, it is now possible to study the disease many years before its clinical onset. Such studies can provide key knowledge regarding pathways for either the prevention of pathology or the postponement of its clinical expression. The former "resistance pathway" suggests that modifiable risk factors could affect amyloid and tau burden decades before the appearance of cognitive impairment. Alternatively, the resilience pathway suggests that modifiable risk factors may mitigate the symptomatic expression of AD pathology on cognition. These pathways are not mutually exclusive and may appear at different disease stages. Here, in a narrative review, we present neuroimaging evidence that supports both pathways in sporadic AD and autosomal-dominant AD. We then propose mechanisms for their protective effect. Among possible mechanisms, we examine neural and vascular mechanisms for the resistance pathway. We also describe brain maintenance and functional compensation as bases for the resilience pathway. Improved mechanistic understanding of both pathways may suggest new interventions.
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Affiliation(s)
- Valentin Ourry
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada.
| | - Alexa Pichet Binette
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada; Clinical Memory Research Unit, Department of Clinical Sciences, Lunds Universitet, Malmö, Sweden
| | - Frédéric St-Onge
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Cherie Strikwerda-Brown
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada; School of Psychological Science, The University of Western Australia, Perth, Western Australia, Australia
| | - Audrey Chagnot
- UK Dementia Research Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Judes Poirier
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - John Breitner
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Eider M Arenaza-Urquijo
- Environment and Health over the Lifecourse Programme, Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Jennifer S Rabin
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada; Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada; Rehabilitation Sciences Institute, University of Toronto, Toronto, Ontario, Canada
| | - Rachel Buckley
- Melbourne School of Psychological Sciences University of Melbourne, Parkville, Victoria, Australia; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Julie Gonneaud
- Normandie University, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders," Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, Caen, France
| | - Natalie L Marchant
- Division of Psychiatry, University College London, London, United Kingdom
| | - Sylvia Villeneuve
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
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25
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Dear R, Wagstyl K, Seidlitz J, Markello RD, Arnatkevičiūtė A, Anderson KM, Bethlehem RAI, Raznahan A, Bullmore ET, Vértes PE. Cortical gene expression architecture links healthy neurodevelopment to the imaging, transcriptomics and genetics of autism and schizophrenia. Nat Neurosci 2024; 27:1075-1086. [PMID: 38649755 PMCID: PMC11156586 DOI: 10.1038/s41593-024-01624-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 03/18/2024] [Indexed: 04/25/2024]
Abstract
Human brain organization involves the coordinated expression of thousands of genes. For example, the first principal component (C1) of cortical transcription identifies a hierarchy from sensorimotor to association regions. In this study, optimized processing of the Allen Human Brain Atlas revealed two new components of cortical gene expression architecture, C2 and C3, which are distinctively enriched for neuronal, metabolic and immune processes, specific cell types and cytoarchitectonics, and genetic variants associated with intelligence. Using additional datasets (PsychENCODE, Allen Cell Atlas and BrainSpan), we found that C1-C3 represent generalizable transcriptional programs that are coordinated within cells and differentially phased during fetal and postnatal development. Autism spectrum disorder and schizophrenia were specifically associated with C1/C2 and C3, respectively, across neuroimaging, differential expression and genome-wide association studies. Evidence converged especially in support of C3 as a normative transcriptional program for adolescent brain development, which can lead to atypical supragranular cortical connectivity in people at high genetic risk for schizophrenia.
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Affiliation(s)
- Richard Dear
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | | | - Jakob Seidlitz
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Ross D Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Aurina Arnatkevičiūtė
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | | | | | - Armin Raznahan
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, USA
| | | | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
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26
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Huang Q, Wang Y, Chen S, Liang F. Glycometabolic Reprogramming of Microglia in Neurodegenerative Diseases: Insights from Neuroinflammation. Aging Dis 2024; 15:1155-1175. [PMID: 37611905 PMCID: PMC11081147 DOI: 10.14336/ad.2023.0807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 08/07/2023] [Indexed: 08/25/2023] Open
Abstract
Neurodegenerative diseases (ND) are conditions defined by progressive deterioration of the structure and function of the nervous system. Some major examples include Alzheimer's disease (AD), Parkinson's disease (PD), and Amyotrophic lateral sclerosis (ALS). These diseases lead to various dysfunctions, like impaired cognition, memory, and movement. Chronic neuroinflammation may underlie numerous neurodegenerative disorders. Microglia, an important immunocell in the brain, plays a vital role in defending against neuroinflammation. When exposed to different stimuli, microglia are activated and assume different phenotypes, participating in immune regulation of the nervous system and maintaining tissue homeostasis. The immunological activity of activated microglia is affected by glucose metabolic alterations. However, in the context of chronic neuroinflammation, specific alterations of microglial glucose metabolism and their mechanisms of action remain unclear. Thus, in this paper, we review the glycometabolic reprogramming of microglia in ND. The key molecular targets and main metabolic pathways are the focus of this research. Additionally, this study explores the mechanisms underlying microglial glucose metabolism reprogramming in ND and offers an analysis of the most recent therapeutic advancements. The ultimate aim is to provide insights into the development of potential treatments for ND.
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Affiliation(s)
- Qi Huang
- Department of Rehabilitation, The Central Hospital of Wuhan, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China.
| | - Yanfu Wang
- Department of Rehabilitation, The Central Hospital of Wuhan, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China.
| | - Shanshan Chen
- Key Laboratory for Molecular Diagnosis of Hubei Province, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Fengxia Liang
- Department of Acupuncture and Moxibustion, Hubei University of Chinese Medicine, Wuhan, China
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27
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Guo B, Li QY, Liu XJ, Luo GH, Wu YJ, Nie J. Diabetes mellitus and Alzheimer's disease: Vacuolar adenosine triphosphatase as a potential link. Eur J Neurosci 2024; 59:2577-2595. [PMID: 38419188 DOI: 10.1111/ejn.16286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 03/02/2024]
Abstract
Globally, the incidence of diabetes mellitus (DM) and Alzheimer's disease (AD) is increasing year by year, causing a huge economic and social burden, and their pathogenesis and aetiology have been proven to have a certain correlation. In recent years, more and more studies have shown that vacuolar adenosine triphosphatases (v-ATPases) in eukaryotes, which are biomolecules regulating lysosomal acidification and glycolipid metabolism, play a key role in DM and AD. This article describes the role of v-ATPase in DM and AD, including its role in glycolysis, insulin secretion and insulin resistance (IR), as well as its relationship with lysosomal acidification, autophagy and β-amyloid (Aβ). In DM, v-ATPase is involved in the regulation of glucose metabolism and IR. v-ATPase is closely related to glycolysis. On the one hand, v-ATPase affects the rate of glycolysis by affecting the secretion of insulin and changing the activities of key glycolytic enzymes hexokinase (HK) and phosphofructokinase 1 (PFK-1). On the other hand, glucose is the main regulator of this enzyme, and the assembly and activity of v-ATPase depend on glucose, and glucose depletion will lead to its decomposition and inactivation. In addition, v-ATPase can also regulate free fatty acids, thereby improving IR. In AD, v-ATPase can not only improve the abnormal brain energy metabolism by affecting lysosomal acidification and autophagy but also change the deposition of Aβ by affecting the production and degradation of Aβ. Therefore, v-ATPase may be the bridge between DM and AD.
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Affiliation(s)
- Bin Guo
- Key Laboratory of Basic Pharmacology of the Ministry of Education and Joint International Research Laboratory of Ethnomedicine of the Ministry of Education, Zunyi Medical University, Zunyi, Guizhou, China
| | - Qi-Ye Li
- Key Laboratory of Basic Pharmacology of the Ministry of Education and Joint International Research Laboratory of Ethnomedicine of the Ministry of Education, Zunyi Medical University, Zunyi, Guizhou, China
| | - Xue-Jia Liu
- Key Laboratory of Basic Pharmacology of the Ministry of Education and Joint International Research Laboratory of Ethnomedicine of the Ministry of Education, Zunyi Medical University, Zunyi, Guizhou, China
| | - Guo-Hui Luo
- Key Laboratory of Basic Pharmacology of the Ministry of Education and Joint International Research Laboratory of Ethnomedicine of the Ministry of Education, Zunyi Medical University, Zunyi, Guizhou, China
| | - Ya-Juan Wu
- Key Laboratory of Basic Pharmacology of the Ministry of Education and Joint International Research Laboratory of Ethnomedicine of the Ministry of Education, Zunyi Medical University, Zunyi, Guizhou, China
| | - Jing Nie
- Key Laboratory of Basic Pharmacology of the Ministry of Education and Joint International Research Laboratory of Ethnomedicine of the Ministry of Education, Zunyi Medical University, Zunyi, Guizhou, China
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28
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Luo AC, Sydnor VJ, Pines A, Larsen B, Alexander-Bloch AF, Cieslak M, Covitz S, Chen AA, Esper NB, Feczko E, Franco AR, Gur RE, Gur RC, Houghton A, Hu F, Keller AS, Kiar G, Mehta K, Salum GA, Tapera T, Xu T, Zhao C, Salo T, Fair DA, Shinohara RT, Milham MP, Satterthwaite TD. Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy. Nat Commun 2024; 15:3511. [PMID: 38664387 PMCID: PMC11045762 DOI: 10.1038/s41467-024-47748-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.
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Affiliation(s)
- Audrey C Luo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sydney Covitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Andrew A Chen
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA
| | | | - Eric Feczko
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Alexandre R Franco
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Fengling Hu
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Gregory Kiar
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Kahini Mehta
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Giovanni A Salum
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Tinashe Tapera
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, 02115, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Chenying Zhao
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Taylor Salo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
- Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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29
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Cleland NRW, Potter GJ, Buck C, Quang D, Oldham D, Neal M, Saviola A, Niemeyer CS, Dobrinskikh E, Bruce KD. Altered metabolism and DAM-signatures in female brains and microglia with aging. Brain Res 2024; 1829:148772. [PMID: 38244754 DOI: 10.1016/j.brainres.2024.148772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 12/21/2023] [Accepted: 01/15/2024] [Indexed: 01/22/2024]
Abstract
Despite Alzheimer's disease (AD) disproportionately affecting women, the mechanisms remain elusive. In AD, microglia undergo 'metabolic reprogramming', which contributes to microglial dysfunction and AD pathology. However, how sex and age contribute to metabolic reprogramming in microglia is understudied. Here, we use metabolic imaging, transcriptomics, and metabolic assays to probe age- and sex-associated changes in brain and microglial metabolism. Glycolytic and oxidative metabolism in the whole brain was determined using Fluorescence Lifetime Imaging Microscopy (FLIM). Young female brains appeared less glycolytic than male brains, but with aging, the female brain became 'male-like.' Transcriptomic analysis revealed increased expression of disease-associated microglia (DAM) genes (e.g., ApoE, Trem2, LPL), and genes involved in glycolysis and oxidative metabolism in microglia from aged females compared to males. To determine whether estrogen can alter the expression of these genes, BV-2 microglia-like cell lines, which abundantly express DAM genes, were supplemented with 17β-estradiol (E2). E2 supplementation resulted in reduced expression of DAM genes, reduced lipid and cholesterol transport, and substrate-dependent changes in glycolysis and oxidative metabolism. Consistent with the notion that E2 may suppress DAM-associated factors, LPL activity was elevated in the brains of aged female mice. Similarly, DAM gene and protein expression was higher in monocyte-derived microglia-like (MDMi) cells derived from middle-aged females compared to age-matched males and was responsive to E2 supplementation. FLIM analysis of MDMi from young and middle-aged females revealed reduced oxidative metabolism and FAD+ with age. Overall, our findings show that altered metabolism defines age-associated changes in female microglia and suggest that estrogen may inhibit the expression and activity of DAM-associated factors, which may contribute to increased AD risk, especially in post-menopausal women.
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Affiliation(s)
- Nicholas R W Cleland
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Garrett J Potter
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Courtney Buck
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Daphne Quang
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Dean Oldham
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Mikaela Neal
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Anthony Saviola
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Christy S Niemeyer
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Evgenia Dobrinskikh
- Section of Neonatology, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, USA
| | - Kimberley D Bruce
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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Ouyang M, Detre JA, Hyland JL, Sindabizera KL, Kuschner ES, Edgar JC, Peng Y, Huang H. Spatiotemporal cerebral blood flow dynamics underlies emergence of the limbic-sensorimotor-association cortical gradient in human infancy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588784. [PMID: 38645183 PMCID: PMC11030426 DOI: 10.1101/2024.04.10.588784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Infant cerebral blood flow (CBF) delivers nutrients and oxygen to fulfill brain energy consumption requirements for the fastest period of postnatal brain development across lifespan. However, organizing principle of whole-brain CBF dynamics during infancy remains obscure. Leveraging a unique cohort of 100+ infants with high-resolution arterial spin labeled MRI, we found the emergence of the cortical hierarchy revealed by highest-resolution infant CBF maps available to date. Infant CBF across cortical regions increased in a biphasic pattern with initial rapid and sequentially slower rate, with break-point ages increasing along the limbic-sensorimotor-association cortical gradient. Increases in CBF in sensorimotor cortices were associated with enhanced language and motor skills, and frontoparietal association cortices for cognitive skills. The study discovered emergence of the hierarchical limbic-sensorimotor-association cortical gradient in infancy, and offers standardized reference of infant brain CBF and insight into the physiological basis of cortical specialization and real-world infant developmental functioning.
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Affiliation(s)
- Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, United States
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - John A Detre
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Jessica L Hyland
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, United States
| | - Kay L Sindabizera
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, United States
| | - Emily S Kuschner
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, United States
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - J Christopher Edgar
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, United States
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Yun Peng
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, Beijing, 100045, China
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, United States
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
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31
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Hahn A, Reed MB, Vraka C, Godbersen GM, Klug S, Komorowski A, Falb P, Nics L, Traub-Weidinger T, Hacker M, Lanzenberger R. High-temporal resolution functional PET/MRI reveals coupling between human metabolic and hemodynamic brain response. Eur J Nucl Med Mol Imaging 2024; 51:1310-1322. [PMID: 38052927 PMCID: PMC11399190 DOI: 10.1007/s00259-023-06542-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/22/2023] [Indexed: 12/07/2023]
Abstract
PURPOSE Positron emission tomography (PET) provides precise molecular information on physiological processes, but its low temporal resolution is a major obstacle. Consequently, we characterized the metabolic response of the human brain to working memory performance using an optimized functional PET (fPET) framework at a temporal resolution of 3 s. METHODS Thirty-five healthy volunteers underwent fPET with [18F]FDG bolus plus constant infusion, 19 of those at a hybrid PET/MRI scanner. During the scan, an n-back working memory paradigm was completed. fPET data were reconstructed to 3 s temporal resolution and processed with a novel sliding window filter to increase signal to noise ratio. BOLD fMRI signals were acquired at 2 s. RESULTS Consistent with simulated kinetic modeling, we observed a constant increase in the [18F]FDG signal during task execution, followed by a rapid return to baseline after stimulation ceased. These task-specific changes were robustly observed in brain regions involved in working memory processing. The simultaneous acquisition of BOLD fMRI revealed that the temporal coupling between hemodynamic and metabolic signals in the primary motor cortex was related to individual behavioral performance during working memory. Furthermore, task-induced BOLD deactivations in the posteromedial default mode network were accompanied by distinct temporal patterns in glucose metabolism, which were dependent on the metabolic demands of the corresponding task-positive networks. CONCLUSIONS In sum, the proposed approach enables the advancement from parallel to truly synchronized investigation of metabolic and hemodynamic responses during cognitive processing. This allows to capture unique information in the temporal domain, which is not accessible to conventional PET imaging.
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Affiliation(s)
- Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria.
| | - Murray B Reed
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Chrysoula Vraka
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Godber M Godbersen
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Sebastian Klug
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Arkadiusz Komorowski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Pia Falb
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Lukas Nics
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Tatjana Traub-Weidinger
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria.
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32
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Luppi AI, Rosas FE, Noonan MP, Mediano PAM, Kringelbach ML, Carhart-Harris RL, Stamatakis EA, Vernon AC, Turkheimer FE. Oxygen and the Spark of Human Brain Evolution: Complex Interactions of Metabolism and Cortical Expansion across Development and Evolution. Neuroscientist 2024; 30:173-198. [PMID: 36476177 DOI: 10.1177/10738584221138032] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Scientific theories on the functioning and dysfunction of the human brain require an understanding of its development-before and after birth and through maturation to adulthood-and its evolution. Here we bring together several accounts of human brain evolution by focusing on the central role of oxygen and brain metabolism. We argue that evolutionary expansion of human transmodal association cortices exceeded the capacity of oxygen delivery by the vascular system, which led these brain tissues to rely on nonoxidative glycolysis for additional energy supply. We draw a link between the resulting lower oxygen tension and its effect on cytoarchitecture, which we posit as a key driver of genetic developmental programs for the human brain-favoring lower intracortical myelination and the presence of biosynthetic materials for synapse turnover. Across biological and temporal scales, this protracted capacity for neural plasticity sets the conditions for cognitive flexibility and ongoing learning, supporting complex group dynamics and intergenerational learning that in turn enabled improved nutrition to fuel the metabolic costs of further cortical expansion. Our proposed model delineates explicit mechanistic links among metabolism, molecular and cellular brain heterogeneity, and behavior, which may lead toward a clearer understanding of brain development and its disorders.
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Affiliation(s)
- Andrea I Luppi
- Department of Clinical Neurosciences and Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - Fernando E Rosas
- Department of Informatics, University of Sussex, Brighton, UK
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London, UK
- Centre for Complexity Science, Imperial College London, London, UK
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - MaryAnn P Noonan
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Pedro A M Mediano
- Department of Psychology, University of Cambridge, Cambridge, UK
- Department of Psychology, Queen Mary University of London, London, UK
- Department of Computing, Imperial College London, London, UK
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Robin L Carhart-Harris
- Psychedelics Division-Neuroscape, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Emmanuel A Stamatakis
- Department of Clinical Neurosciences and Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Anthony C Vernon
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Federico E Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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33
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Su S, Zhao J, Dai Y, Lin L, Zhou Q, Yan Z, Qian L, Cui W, Liu M, Zhang H, Yang Z, Chen Y. Altered neurovascular coupling in the children with attention-deficit/hyperactivity disorder: a comprehensive fMRI analysis. Eur Child Adolesc Psychiatry 2024; 33:1081-1091. [PMID: 37222790 DOI: 10.1007/s00787-023-02238-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 05/17/2023] [Indexed: 05/25/2023]
Abstract
The coupling between resting-state cerebral blood flow (CBF) and blood oxygenation level-dependent (BOLD) signals reflects the mechanism of neurovascular coupling (NVC), which have not been illustrated in attention-deficit/hyperactivity disorder (ADHD). Fifty ADHD and 42 age- and gender-matched typically developing controls (TDs) were enrolled. The NVC imaging metrics were investigated by exploring the Pearson correlation coefficients between CBF and BOLD-derived quantitative maps (ALFF, fALFF, DCP maps). Three types of NVC metrics (CBF-ALFF, CBF-fALFF, CBF-DCP coupling) were compared between ADHD and TDs group, and the inner association between altered NVC metrics and clinical variables in ADHD group was further analyzed. Compared to TDs, ADHD showed significantly reduced whole-brain CBF-ALFF coupling (P < 0.001). Among regional level (all PFDR < 0.05), ADHD showed significantly lower CBF-ALFF coupling in bilateral thalamus, default-mode network (DMN) involving left anterior cingulate (ACG.L) and right parahippocampal gyrus (PHG.R), execution control network (ECN) involving right middle orbital frontal gyrus (ORBmid.R) and right inferior frontal triangular gyrus (IFGtriang.R), and increased CBF-ALFF coupling in attention network (AN)-related left superior temporal gyrus (STG.L) and somatosensory network (SSN))-related left rolandic operculum (ROL.L). Furthermore, increased CBF-fALFF coupling was found in the visual network (VN)-related left cuneus and negatively correlated with the concentration index of ADHD (R = - 0.299, PFDR = 0.035). Abnormal regional NVC metrics were at widespread neural networks in ADHD, mainly involved in DMN, ECN, SSN, AN, VN and bilateral thalamus. Notably, this study reinforced the insights into the neural basis and pathophysiological mechanism underlying ADHD.
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Affiliation(s)
- Shu Su
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jing Zhao
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yan Dai
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Liping Lin
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Qin Zhou
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Zi Yan
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Long Qian
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China
| | - Wei Cui
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China
| | - Meina Liu
- Department of Pediatric, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Hongyu Zhang
- Department of Pediatric, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Zhiyun Yang
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
| | - Yingqian Chen
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
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Jiang Y, Li W, Li J, Li X, Zhang H, Sima X, Li L, Wang K, Li Q, Fang J, Jin L, Gong Q, Yao D, Zhou D, Luo C, An D. Identification of four biotypes in temporal lobe epilepsy via machine learning on brain images. Nat Commun 2024; 15:2221. [PMID: 38472252 DOI: 10.1038/s41467-024-46629-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 03/05/2024] [Indexed: 03/14/2024] Open
Abstract
Artificial intelligence provides an opportunity to try to redefine disease subtypes based on similar pathobiology. Using a machine-learning algorithm (Subtype and Stage Inference) with cross-sectional MRI from 296 individuals with focal epilepsy originating from the temporal lobe (TLE) and 91 healthy controls, we show phenotypic heterogeneity in the pathophysiological progression of TLE. This study was registered in the Chinese Clinical Trials Registry (number: ChiCTR2200062562). We identify two hippocampus-predominant phenotypes, characterized by atrophy beginning in the left or right hippocampus; a third cortex-predominant phenotype, characterized by hippocampus atrophy after the neocortex; and a fourth phenotype without atrophy but amygdala enlargement. These four subtypes are replicated in the independent validation cohort (109 individuals). These subtypes show differences in neuroanatomical signature, disease progression and epilepsy characteristics. Five-year follow-up observations of these individuals reveal differential seizure outcomes among subtypes, indicating that specific subtypes may benefit from temporal surgery or pharmacological treatment. These findings suggest a diverse pathobiological basis underlying focal epilepsy that potentially yields to stratification and prognostication - a necessary step for precise medicine.
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Affiliation(s)
- Yuchao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
| | - Wei Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Geriatrics, West China Hospital, Sichuan University, China National Clinical Research Center for Geriatric Medicine, Chengdu, China
| | - Jinmei Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiuli Li
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Heng Zhang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiutian Sima
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Luying Li
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Kang Wang
- Epilepsy Center, Department of Neurology, The first affiliated hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qifu Li
- Department of Neurology, The first affiliated hospital, Hainan Medical University and the Key Laboratory of Brain Science Research and Transformation in Tropical Environment of Hainan Province, Haikou, Hainan, China
| | - Jiajia Fang
- Department of Neurology, The fourth affiliated hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang, China
| | - Lu Jin
- Psychological Medicine Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and technology, University of Electronic Science and Technology of China, Chengdu, China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, China
| | - Dong Zhou
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and technology, University of Electronic Science and Technology of China, Chengdu, China.
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China.
- Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, China.
| | - Dongmei An
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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35
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Betzel R, Puxeddu MG, Seguin C, Bazinet V, Luppi A, Podschun A, Singleton SP, Faskowitz J, Parakkattu V, Misic B, Markett S, Kuceyeski A, Parkes L. Controlling the human connectome with spatially diffuse input signals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.581006. [PMID: 38463980 PMCID: PMC10925126 DOI: 10.1101/2024.02.27.581006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The human brain is never at "rest"; its activity is constantly fluctuating over time, transitioning from one brain state-a whole-brain pattern of activity-to another. Network control theory offers a framework for understanding the effort - energy - associated with these transitions. One branch of control theory that is especially useful in this context is "optimal control", in which input signals are used to selectively drive the brain into a target state. Typically, these inputs are introduced independently to the nodes of the network (each input signal is associated with exactly one node). Though convenient, this input strategy ignores the continuity of cerebral cortex - geometrically, each region is connected to its spatial neighbors, allowing control signals, both exogenous and endogenous, to spread from their foci to nearby regions. Additionally, the spatial specificity of brain stimulation techniques is limited, such that the effects of a perturbation are measurable in tissue surrounding the stimulation site. Here, we adapt the network control model so that input signals have a spatial extent that decays exponentially from the input site. We show that this more realistic strategy takes advantage of spatial dependencies in structural connectivity and activity to reduce the energy (effort) associated with brain state transitions. We further leverage these dependencies to explore near-optimal control strategies such that, on a per-transition basis, the number of input signals required for a given control task is reduced, in some cases by two orders of magnitude. This approximation yields network-wide maps of input site density, which we compare to an existing database of functional, metabolic, genetic, and neurochemical maps, finding a close correspondence. Ultimately, not only do we propose a more efficient framework that is also more adherent to well-established brain organizational principles, but we also posit neurobiologically grounded bases for optimal control.
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Affiliation(s)
- Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
- Cognitive Science Program, Indiana University, Bloomington IN 47401
- Program in Neuroscience, Indiana University, Bloomington IN 47401
| | - Maria Grazia Puxeddu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
| | - Vincent Bazinet
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Andrea Luppi
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | | | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
| | - Vibin Parakkattu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
- Cognitive Science Program, Indiana University, Bloomington IN 47401
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY
- Department of Computational Biology, Cornell University, Ithaca, NY
| | - Linden Parkes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
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Gustafsson J, Robinson JL, Zetterberg H, Nielsen J. Brain energy metabolism is optimized to minimize the cost of enzyme synthesis and transport. Proc Natl Acad Sci U S A 2024; 121:e2305035121. [PMID: 38315844 PMCID: PMC10873630 DOI: 10.1073/pnas.2305035121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 12/22/2023] [Indexed: 02/07/2024] Open
Abstract
The energy metabolism of the brain is poorly understood partly due to the complex morphology of neurons and fluctuations in ATP demand over time. To investigate this, we used metabolic models that estimate enzyme usage per pathway, enzyme utilization over time, and enzyme transportation to evaluate how these parameters and processes affect ATP costs for enzyme synthesis and transportation. Our models show that the total enzyme maintenance energy expenditure of the human body depends on how glycolysis and mitochondrial respiration are distributed both across and within cell types in the brain. We suggest that brain metabolism is optimized to minimize the ATP maintenance cost by distributing the different ATP generation pathways in an advantageous way across cell types and potentially also across synapses within the same cell. Our models support this hypothesis by predicting export of lactate from both neurons and astrocytes during peak ATP demand, reproducing results from experimental measurements reported in the literature. Furthermore, our models provide potential explanation for parts of the astrocyte-neuron lactate shuttle theory, which is recapitulated under some conditions in the brain, while contradicting other aspects of the theory. We conclude that enzyme usage per pathway, enzyme utilization over time, and enzyme transportation are important factors for defining the optimal distribution of ATP production pathways, opening a broad avenue to explore in brain metabolism.
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Affiliation(s)
- Johan Gustafsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Jonathan L. Robinson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- BioInnovation Institute, CopenhagenDK-2200, Denmark
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Mölndal431 30, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal431 30, Sweden
- Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, LondonWC1E 6BT, United Kingdom
- United Kingdom Dementia Research Institute, University College London, LondonWC1E 6BT, United Kingdom
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong999077, China
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI53792
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Mölndal431 30, Sweden
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Luppi AI, Girn M, Rosas FE, Timmermann C, Roseman L, Erritzoe D, Nutt DJ, Stamatakis EA, Spreng RN, Xing L, Huttner WB, Carhart-Harris RL. A role for the serotonin 2A receptor in the expansion and functioning of human transmodal cortex. Brain 2024; 147:56-80. [PMID: 37703310 DOI: 10.1093/brain/awad311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/14/2023] [Accepted: 08/18/2023] [Indexed: 09/15/2023] Open
Abstract
Integrating independent but converging lines of research on brain function and neurodevelopment across scales, this article proposes that serotonin 2A receptor (5-HT2AR) signalling is an evolutionary and developmental driver and potent modulator of the macroscale functional organization of the human cerebral cortex. A wealth of evidence indicates that the anatomical and functional organization of the cortex follows a unimodal-to-transmodal gradient. Situated at the apex of this processing hierarchy-where it plays a central role in the integrative processes underpinning complex, human-defining cognition-the transmodal cortex has disproportionately expanded across human development and evolution. Notably, the adult human transmodal cortex is especially rich in 5-HT2AR expression and recent evidence suggests that, during early brain development, 5-HT2AR signalling on neural progenitor cells stimulates their proliferation-a critical process for evolutionarily-relevant cortical expansion. Drawing on multimodal neuroimaging and cross-species investigations, we argue that, by contributing to the expansion of the human cortex and being prevalent at the apex of its hierarchy in the adult brain, 5-HT2AR signalling plays a major role in both human cortical expansion and functioning. Owing to its unique excitatory and downstream cellular effects, neuronal 5-HT2AR agonism promotes neuroplasticity, learning and cognitive and psychological flexibility in a context-(hyper)sensitive manner with therapeutic potential. Overall, we delineate a dual role of 5-HT2ARs in enabling both the expansion and modulation of the human transmodal cortex.
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Affiliation(s)
- Andrea I Luppi
- Department of Clinical Neurosciences and Division of Anaesthesia, University of Cambridge, Cambridge, CB2 0QQ, UK
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, CB2 1SB, UK
- The Alan Turing Institute, London, NW1 2DB, UK
| | - Manesh Girn
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, H3A 2B4, Canada
- Psychedelics Division-Neuroscape, Department of Neurology, University of California SanFrancisco, San Francisco, CA 94158, USA
| | - Fernando E Rosas
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
- Data Science Institute, Imperial College London, London, SW7 2AZ, UK
- Centre for Complexity Science, Imperial College London, London, SW7 2AZ, UK
| | - Christopher Timmermann
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - Leor Roseman
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - David Erritzoe
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - David J Nutt
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - Emmanuel A Stamatakis
- Department of Clinical Neurosciences and Division of Anaesthesia, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - R Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, H3A 2B4, Canada
| | - Lei Xing
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, 01307, Germany
| | - Wieland B Huttner
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, 01307, Germany
| | - Robin L Carhart-Harris
- Psychedelics Division-Neuroscape, Department of Neurology, University of California SanFrancisco, San Francisco, CA 94158, USA
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
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38
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Matt RA, Martin RS, Evans AK, Gever JR, Vargas GA, Shamloo M, Ford AP. Locus Coeruleus and Noradrenergic Pharmacology in Neurodegenerative Disease. Handb Exp Pharmacol 2024; 285:555-616. [PMID: 37495851 DOI: 10.1007/164_2023_677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Adrenoceptors (ARs) throughout the brain are stimulated by noradrenaline originating mostly from neurons of the locus coeruleus, a brainstem nucleus that is ostensibly the earliest to show detectable pathology in neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. The α1-AR, α2-AR, and β-AR subtypes expressed in target brain regions and on a range of cell populations define the physiological responses to noradrenaline, which includes activation of cognitive function in addition to modulation of neurometabolism, cerebral blood flow, and neuroinflammation. As these heterocellular functions are critical for maintaining brain homeostasis and neuronal health, combating the loss of noradrenergic tone from locus coeruleus degeneration may therefore be an effective treatment for both cognitive symptoms and disease modification in neurodegenerative indications. Two pharmacologic approaches are receiving attention in recent clinical studies: preserving noradrenaline levels (e.g., via reuptake inhibition) and direct activation of target adrenoceptors. Here, we review the expression and role of adrenoceptors in the brain, the preclinical studies which demonstrate that adrenergic stimulation can support cognitive function and cerebral health by reversing the effects of noradrenaline depletion, and the human data provided by pharmacoepidemiologic analyses and clinical trials which together identify adrenoceptors as promising targets for the treatment of neurodegenerative disease.
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Affiliation(s)
| | | | - Andrew K Evans
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA, USA
| | | | | | - Mehrdad Shamloo
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA, USA
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39
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Adamson PM, Datta K, Watkins R, Recht LD, Hurd RE, Spielman DM. Deuterium metabolic imaging for 3D mapping of glucose metabolism in humans with central nervous system lesions at 3T. Magn Reson Med 2024; 91:39-50. [PMID: 37796151 PMCID: PMC10841984 DOI: 10.1002/mrm.29830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/19/2023] [Accepted: 07/28/2023] [Indexed: 10/06/2023]
Abstract
PURPOSE To explore the potential of 3T deuterium metabolic imaging (DMI) using a birdcage 2 H radiofrequency (RF) coil in both healthy volunteers and patients with central nervous system (CNS) lesions. METHODS A modified gradient filter, home-built 2 H volume RF coil, and spherical k-space sampling were employed in a three-dimensional chemical shift imaging acquisition to obtain high-quality whole-brain metabolic images of 2 H-labeled water and glucose metabolic products. These images were acquired in a healthy volunteer and three subjects with CNS lesions of varying pathologies. Hardware and pulse sequence experiments were also conducted to improve the signal-to-noise ratio of DMI at 3T. RESULTS The ability to quantify local glucose metabolism in correspondence to anatomical landmarks across patients with varying CNS lesions is demonstrated, and increased lactate is observed in one patient with the most active disease. CONCLUSION DMI offers the potential to examine metabolic activity in human subjects with CNS lesions with DMI at 3T, promising for the potential of the future clinical translation of this metabolic imaging technique.
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Affiliation(s)
- Philip M. Adamson
- Department of Electrical Engineering, Stanford University, Stanford, California USA
| | - Keshav Datta
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Ron Watkins
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Lawrence D. Recht
- Department of Neurology, Stanford University, Stanford, California, USA
| | - Ralph E. Hurd
- Department of Radiology, Stanford University, Stanford, California, USA
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40
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Bazinet V, Hansen JY, Misic B. Towards a biologically annotated brain connectome. Nat Rev Neurosci 2023; 24:747-760. [PMID: 37848663 DOI: 10.1038/s41583-023-00752-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2023] [Indexed: 10/19/2023]
Abstract
The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.
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Affiliation(s)
- Vincent Bazinet
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Justine Y Hansen
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada.
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41
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Clairis N, Lopez-Persem A. Debates on the dorsomedial prefrontal/dorsal anterior cingulate cortex: insights for future research. Brain 2023; 146:4826-4844. [PMID: 37530487 PMCID: PMC10690029 DOI: 10.1093/brain/awad263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 07/19/2023] [Accepted: 07/22/2023] [Indexed: 08/03/2023] Open
Abstract
The dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC) is a brain area subject to many theories and debates over its function(s). Even its precise anatomical borders are subject to much controversy. In the past decades, the dmPFC/dACC has been associated with more than 15 different cognitive processes, which sometimes appear quite unrelated (e.g. body perception, cognitive conflict). As a result, understanding what the dmPFC/dACC does has become a real challenge for many neuroscientists. Several theories of this brain area's function(s) have been developed, leading to successive and competitive publications bearing different models, which sometimes contradict each other. During the last two decades, the lively scientific exchanges around the dmPFC/dACC have promoted fruitful research in cognitive neuroscience. In this review, we provide an overview of the anatomy of the dmPFC/dACC, summarize the state of the art of functions that have been associated with this brain area and present the main theories aiming at explaining the dmPFC/dACC function(s). We explore the commonalities and the arguments between the different theories. Finally, we explain what can be learned from these debates for future investigations of the dmPFC/dACC and other brain regions' functions.
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Affiliation(s)
- Nicolas Clairis
- Laboratory of Behavioral Genetics (LGC)- Brain Mind Institute (BMI)- Sciences de la Vie (SV), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Alizée Lopez-Persem
- FrontLab, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Sorbonne University, AP HP, Hôpital de la Pitié Salpêtrière, 75013 Paris, France
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42
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Hu JY, Vaziri S, Bøgh N, Kim Y, Autry AW, Bok RA, Li Y, Laustsen C, Xu D, Larson PEZ, Chang S, Vigneron DB, Gordon JW. Investigating cerebral perfusion with high resolution hyperpolarized [1- 13 C]pyruvate MRI. Magn Reson Med 2023; 90:2233-2241. [PMID: 37665726 PMCID: PMC10543485 DOI: 10.1002/mrm.29844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE To investigate high-resolution hyperpolarized (HP) 13 C pyruvate MRI for measuring cerebral perfusion in the human brain. METHODS HP [1-13 C]pyruvate MRI was acquired in five healthy volunteers with a multi-resolution EPI sequence with 7.5 × 7.5 mm2 resolution for pyruvate. Perfusion parameters were calculated from pyruvate MRI using block-circulant singular value decomposition and compared to relative cerebral blood flow calculated from arterial spin labeling (ASL). To examine regional perfusion patterns, correlations between pyruvate and ASL perfusion were performed for whole brain, gray matter, and white matter voxels. RESULTS High resolution 7.5 × 7.5 mm2 pyruvate images were used to obtain relative cerebral blood flow (rCBF) values that were significantly positively correlated with ASL rCBF values (r = 0.48, 0.20, 0.28 for whole brain, gray matter, and white matter voxels respectively). Whole brain voxels exhibited the highest correlation between pyruvate and ASL perfusion, and there were distinct regional patterns of relatively high ASL and low pyruvate normalized rCBF found across subjects. CONCLUSION Acquiring HP 13 C pyruvate metabolic images at higher resolution allows for finer spatial delineation of brain structures and can be used to obtain cerebral perfusion parameters. Pyruvate perfusion parameters were positively correlated to proton ASL perfusion values, indicating a relationship between the two perfusion measures. This HP 13 C study demonstrated that hyperpolarized pyruvate MRI can assess cerebral metabolism and perfusion within the same study.
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Affiliation(s)
- Jasmine Y. Hu
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
- UC Berkeley-UCSF Graduate Program in Bioengineering,
University of California, San Francisco and University of California, Berkeley,
California, USA
| | - Sana Vaziri
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
| | - Nikolaj Bøgh
- MR Research Center, Department of Clinical Medicine, Aarhus
University, Aarhus, Denmark
| | - Yaewon Kim
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
| | - Adam W. Autry
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
| | - Robert A. Bok
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
| | - Yan Li
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
- UC Berkeley-UCSF Graduate Program in Bioengineering,
University of California, San Francisco and University of California, Berkeley,
California, USA
| | - Christoffer Laustsen
- MR Research Center, Department of Clinical Medicine, Aarhus
University, Aarhus, Denmark
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
- UC Berkeley-UCSF Graduate Program in Bioengineering,
University of California, San Francisco and University of California, Berkeley,
California, USA
| | - Peder E. Z. Larson
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
- UC Berkeley-UCSF Graduate Program in Bioengineering,
University of California, San Francisco and University of California, Berkeley,
California, USA
| | - Susan Chang
- Department of Neurological Surgery, University of
California San Francisco, San Francisco, California, USA
| | - Daniel B. Vigneron
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
- UC Berkeley-UCSF Graduate Program in Bioengineering,
University of California, San Francisco and University of California, Berkeley,
California, USA
| | - Jeremy W. Gordon
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
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Cleland NRW, Potter GJ, Buck C, Quang D, Oldham D, Neal M, Saviola A, Niemeyer CS, Dobrinskikh E, Bruce KD. Altered Metabolism and DAM-signatures in Female Brains and Microglia with Aging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.28.569104. [PMID: 38076915 PMCID: PMC10705419 DOI: 10.1101/2023.11.28.569104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Despite Alzheimer's disease (AD) disproportionately affecting women, the mechanisms remain elusive. In AD, microglia undergo 'metabolic reprogramming', which contributes to microglial dysfunction and AD pathology. However, how sex and age contribute to metabolic reprogramming in microglia is understudied. Here, we use metabolic imaging, transcriptomics, and metabolic assays to probe age-and sex-associated changes in brain and microglial metabolism. Glycolytic and oxidative metabolism in the whole brain was determined using Fluorescence Lifetime Imaging Microscopy (FLIM). Young female brains appeared less glycolytic than male brains, but with aging, the female brain became 'male-like.' Transcriptomic analysis revealed increased expression of disease-associated microglia (DAM) genes (e.g., ApoE, Trem2, LPL), and genes involved in glycolysis and oxidative metabolism in microglia from aged females compared to males. To determine whether estrogen can alter the expression of these genes, BV-2 microglia-like cell lines, which abundantly express DAM genes, were supplemented with 17β-estradiol (E2). E2 supplementation resulted in reduced expression of DAM genes, reduced lipid and cholesterol transport, and substrate-dependent changes in glycolysis and oxidative metabolism. Consistent with the notion that E2 may suppress DAM-associated factors, LPL activity was elevated in the brains of aged female mice. Similarly, DAM gene and protein expression was higher in monocyte-derived microglia-like (MDMi) cells derived from middle-aged females compared to age-matched males and was responsive to E2 supplementation. FLIM analysis of MDMi from young and middle-aged females revealed reduced oxidative metabolism and FAD+ with age. Overall, our findings show that altered metabolism defines age-associated changes in female microglia and suggest that estrogen may inhibit the expression and activity of DAM-associated factors, which may contribute to increased AD risk, especially in post-menopausal women.
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Affiliation(s)
- Nicholas R W Cleland
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO USA
| | - Garrett J Potter
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO USA
| | - Courtney Buck
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO USA
| | - Daphne Quang
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO USA
| | - Dean Oldham
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO USA
| | - Mikaela Neal
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO USA
| | - Anthony Saviola
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO USA
| | - Christy S. Niemeyer
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Evgenia Dobrinskikh
- Section of Neonatology, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, USA
| | - Kimberley D. Bruce
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO USA
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Seidlitz J, Mallard TT, Vogel JW, Lee YH, Warrier V, Ball G, Hansson O, Hernandez LM, Mandal AS, Wagstyl K, Lombardo MV, Courchesne E, Glessner JT, Satterthwaite TD, Bethlehem RAI, Bernstock JD, Tasaki S, Ng B, Gaiteri C, Smoller JW, Ge T, Gur RE, Gandal MJ, Alexander-Bloch AF. The molecular genetic landscape of human brain size variation. Cell Rep 2023; 42:113439. [PMID: 37963017 DOI: 10.1016/j.celrep.2023.113439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 06/13/2023] [Accepted: 10/27/2023] [Indexed: 11/16/2023] Open
Abstract
Human brain size changes dynamically through early development, peaks in adolescence, and varies up to 2-fold among adults. However, the molecular genetic underpinnings of interindividual variation in brain size remain unknown. Here, we leveraged postmortem brain RNA sequencing and measurements of brain weight (BW) in 2,531 individuals across three independent datasets to identify 928 genome-wide significant associations with BW. Genes associated with higher or lower BW showed distinct neurodevelopmental trajectories and spatial patterns that mapped onto functional and cellular axes of brain organization. Expression of BW genes was predictive of interspecies differences in brain size, and bioinformatic annotation revealed enrichment for neurogenesis and cell-cell communication. Genome-wide, transcriptome-wide, and phenome-wide association analyses linked BW gene sets to neuroimaging measurements of brain size and brain-related clinical traits. Cumulatively, these results represent a major step toward delineating the molecular pathways underlying human brain size variation in health and disease.
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Affiliation(s)
- Jakob Seidlitz
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Jacob W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Younga H Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge CB2 1TN, UK; Department of Psychology, University of Cambridge, Cambridge CB2 1TN, UK
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Melbourne, VIC 3052, Australia
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö P663+Q9, Sweden; Memory Clinic, Skåne University Hospital, Malmö P663+Q9, Sweden
| | - Leanna M Hernandez
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Ayan S Mandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - Michael V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
| | - Eric Courchesne
- Department of Neuroscience, University of California, San Diego, San Diego, CA 92093, USA; Autism Center of Excellence, University of California, San Diego, San Diego, CA 92093, USA
| | - Joseph T Glessner
- The Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | | | - Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard University, Boston, MA 02115, USA; Department of Neurosurgery, Boston Children's Hospital, Harvard University, Boston, MA 02115, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Bernard Ng
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Raquel E Gur
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael J Gandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron F Alexander-Bloch
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
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Pal R, Hui D, Menchen H, Zhao H, Mozziconacci O, Wilkins H, Blagg BSJ, Schöneich C, Swerdlow RH, Michaelis ML, Michaelis EK. Protection against Aβ-induced neuronal damage by KU-32: PDHK1 inhibition as important target. Front Aging Neurosci 2023; 15:1282855. [PMID: 38035268 PMCID: PMC10682733 DOI: 10.3389/fnagi.2023.1282855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 10/23/2023] [Indexed: 12/02/2023] Open
Abstract
A feature of most neurodegenerative diseases is the presence of "mis-folded proteins" that form aggregates, suggesting suboptimal activity of neuronal molecular chaperones. Heat shock protein 90 (Hsp90) is the master regulator of cell responses to "proteotoxic" stresses. Some Hsp90 modulators activate cascades leading to upregulation of additional chaperones. Novobiocin is a modulator at the C-terminal ATP-binding site of Hsp90. Of several novobiocin analogs synthesized and tested for protection against amyloid beta (Aβ)-induced neuronal death, "KU-32" was the most potent in protecting primary neurons, but did not increase expression of other chaperones believed to help clear misfolded proteins. However, KU-32 reversed Aβ-induced superoxide formation, activated Complex I of the electron transfer chain in mitochondria, and blocked the Aβ-induced inhibition of Complex I in neuroblastoma cells. A mechanism for these effects of KU-32 on mitochondrial metabolism appeared to be the inhibition of pyruvate dehydrogenase kinase (PDHK), both in isolated brain mitochondria and in SH-SY5Y cells. PDHK inhibition by the classic enzyme inhibitor, dichloroacetate, led to neuroprotection from Aβ25-35-induced cell injury similarly to KU-32. Inhibition of PDHK in neurons would lead to activation of the PDH complex, increased acetyl-CoA generation, stimulation of the tricarboxylic acid cycle and Complex I in the electron transfer chain, and enhanced oxidative phosphorylation. A focus of future studies may be on the potential value of PDHK as a target in AD therapy.
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Affiliation(s)
- Ranu Pal
- Higuchi Biosciences Center, University of Kansas, Lawrence, KS, United States
| | - Dongwei Hui
- Higuchi Biosciences Center, University of Kansas, Lawrence, KS, United States
- Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS, United States
| | - Heather Menchen
- Higuchi Biosciences Center, University of Kansas, Lawrence, KS, United States
| | - Huiping Zhao
- Department of Medicinal Chemistry, University of Kansas, Lawrence, KS, United States
| | - Olivier Mozziconacci
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas Medical Center, Kansas City, KS, United States
| | - Heather Wilkins
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas Medical Center, Kansas City, KS, United States
| | - Brian S. J. Blagg
- Department of Medicinal Chemistry, University of Kansas, Lawrence, KS, United States
- Department of Chemistry and Biochemistry, University of Notre Dame, South Bend, IN, United States
| | - Christian Schöneich
- Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS, United States
| | - Russell H. Swerdlow
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas Medical Center, Kansas City, KS, United States
| | - Mary L. Michaelis
- Higuchi Biosciences Center, University of Kansas, Lawrence, KS, United States
- Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS, United States
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas Medical Center, Kansas City, KS, United States
| | - Elias K. Michaelis
- Higuchi Biosciences Center, University of Kansas, Lawrence, KS, United States
- Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS, United States
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas Medical Center, Kansas City, KS, United States
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Lee JJ, Earnest T, Ha SM, Bani A, Kothapalli D, Liu P, Sotiras A. Patterns of Glucose Metabolism in [ 18 F]FDG PET Indicate Regional Variability and Neurodegeneration in the Progression of Alzheimer's Dementia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.10.23298396. [PMID: 38116031 PMCID: PMC10729728 DOI: 10.1101/2023.11.10.23298396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
In disorders of cognitive impairment, such as Alzheimer's disease, neurodegeneration is the final common pathway of disease progression. Modulating, reversing, or preventing disease progression is a clinical imperative most likely to succeed following accurate and explanatory understanding of neurodegeneration, requiring enhanced consistency with quantitative measurements and expanded interpretability of complex data. The on-going study of neurodegeneration has robustly demonstrated the advantages of accumulating large amounts of clinical data that include neuroimaging, motiving multi-center studies such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). Demonstrative advantages also arise from highly multivariate analysis methods, and this work reports advances provided by non-negative matrix factorization (NMF). NMF revealed patterns of covariance for glucose metabolism, estimated by positron emission tomography of [ 18 F]fluorodeoxyglucose, in 243 healthy normal participants of ADNI. Patterns for glucose metabolism provided cross-sectional inferences for 860 total participants of ADNI with and without cerebral amyloidosis and clinical dementia ratings (CDR) ranging 0-3. Patterns for glucose metabolism were distinct in number and topography from patterns identified in previous studies of structural MRI. They were also distinct from well-establish topographies of resting-state neuronal networks mapped by functional magnetic resonance imaging. Patterns for glucose metabolism identified significant topographical landmarks relating age, sex, APOE ε4 alleles, amyloidosis, CDR, and neurodegeneration. Patterns involving insular and orbitofrontal cortices, as well as midline regions of frontal and parietal lobes demonstrated the greatest neurodegeneration with progressive Alzheimer's dementia. A single pattern for the lateral parietal and posterior superior temporal cortices demonstrated preserved glucose metabolism for all diagnostic groups, including Alzheimer's dementia. Patterns correlated significantly with topical terms from the Neurosynth platform, thereby providing semantic representations for patterns such as attention, memory, language, fear/reward, movement and motor planning. In summary, NMF is a data-driven, principled, supervised statistical learning method that provides interpretable patterns of neurodegeneration. These patterns can help inform the understanding and treatment of Alzheimer's disease. Highlights ▪ Data-driven non-negative matrix factorization (NMF) identified 24 canonical patterns of spatial covariance of cerebral glucose metabolism. The training data comprised healthy older participants (CDR = 0 without amyloidosis) cross-sectionally drawn from ADNI. ▪ In healthy participants, mean SUVRs for specific patterns in precuneus, lateral parietal cortex, and subcortical areas including superficial white matter and striatum, demonstrated increasing glucose metabolism with advancing age. ▪ In asymptomatic participants with amyloidosis , glucose metabolism increased compared to those who were asymptomatic without amyloid , particularly in medial prefrontal cortex, frontoparietal cortex, occipital white, and posterior cerebellar regions. ▪ In symptomatic participants with amyloidosis , insular cortex, medial frontal cortex, and prefrontal cortex demonstrated the most severe losses of glucose metabolism with increasing CDR. Lateral parietal and posterior superior temporal cortices retained glucose metabolism even for CDR > 0.5. ▪ NMF models of glucose metabolism are consistent with models arising from principal components, or eigenbrains, while adding additional regional interpretability. ▪ NMF patterns correlated with regions catalogued in Neurosynth. Following corrections for spatial autocorrelations, NMF patterns revealed meta-analytic identifications of patterns with Neurosynth topics of fear/reward, attention, memory, language, and movement with motor planning. Patterns varied with degrees of cognitive impairment.
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47
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Tu JC, Millar PR, Strain JF, Eck A, Adeyemo B, Daniels A, Karch C, Huey ED, McDade E, Day GS, Yakushev I, Hassenstab J, Morris J, Llibre-Guerra JJ, Ibanez L, Jucker M, Mendez PC, Bateman RJ, Perrin RJ, Benzinger T, Jack CR, Betzel R, Ances BM, Eggebrecht AT, Gordon BA, Wheelock MD. Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.29.564633. [PMID: 37961586 PMCID: PMC10634945 DOI: 10.1101/2023.10.29.564633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer Disease (AD). Given their essential role in neural communication, disruptions to these hubs have profound implications for overall brain network integrity and functionality. Hub disruption, or targeted impairment of functional connectivity at the hubs, is recognized in AD patients. Computational models paired with evidence from animal experiments hint at a mechanistic explanation, suggesting that these hubs may be preferentially targeted in neurodegeneration, due to their high neuronal activity levels-a phenomenon termed "activity-dependent degeneration". Yet, two critical issues were unresolved. First, past research hasn't definitively shown whether hub regions face a higher likelihood of impairment (targeted attack) compared to other regions or if impairment likelihood is uniformly distributed (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We applied a refined hub disruption index to determine the presence of targeted attacks in AD. Furthermore, we explored potential evidence for activity-dependent degeneration by evaluating if hub vulnerability is better explained by global connectivity or connectivity variations across functional systems, as well as comparing its timing relative to amyloid beta deposition in the brain. Our unique cohort of participants with autosomal dominant Alzheimer Disease (ADAD) allowed us to probe into the preclinical stages of AD to determine the hub disruption timeline in relation to expected symptom emergence. Our findings reveal a hub disruption pattern in ADAD aligned with targeted attacks, detectable even in pre-clinical stages. Notably, the disruption's severity amplified alongside symptomatic progression. Moreover, since excessive local neuronal activity has been shown to increase amyloid deposition and high connectivity regions show high level of neuronal activity, our observation that hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in Aβ PET cortical markers is consistent with the activity-dependent degeneration model. Intriguingly, these disruptions were discernible 8 years before the expected age of symptom onset. Taken together, our findings not only align with the targeted attack on hubs model but also suggest that activity-dependent degeneration might be the cause of hub vulnerability. This deepened understanding could be instrumental in refining diagnostic techniques and developing targeted therapeutic strategies for AD in the future.
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Affiliation(s)
- Jiaxin Cindy Tu
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Peter R Millar
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Jeremy F Strain
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Andrew Eck
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Babatunde Adeyemo
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Alisha Daniels
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Celeste Karch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Edward D Huey
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, 02912
| | - Eric McDade
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Gregory S Day
- Department of Neurology, Mayo Clinic College of Medicine, Jacksonville, FL, USA, 32224
| | - Igor Yakushev
- Department of Nuclear Medicine, Technical University of Munich, Munich, Germany, 81675
| | - Jason Hassenstab
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - John Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Jorge J Llibre-Guerra
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Laura Ibanez
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA, 63108
- NeuroGenomics and Informatics Center, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Mathias Jucker
- Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany, 72076
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany, 72076
| | | | - Randell J Bateman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Richard J Perrin
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Tammie Benzinger
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, USA 55905
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN USA, 47405
| | - Beau M Ances
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Adam T Eggebrecht
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Brian A Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Muriah D Wheelock
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA, 63108
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48
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Volpi T, Vallini G, Silvestri E, Francisci MD, Durbin T, Corbetta M, Lee JJ, Vlassenko AG, Goyal MS, Bertoldo A. A new framework for metabolic connectivity mapping using bolus [ 18F]FDG PET and kinetic modeling. J Cereb Blood Flow Metab 2023; 43:1905-1918. [PMID: 37377103 PMCID: PMC10676136 DOI: 10.1177/0271678x231184365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 04/11/2023] [Accepted: 06/03/2023] [Indexed: 06/29/2023]
Abstract
Metabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [18F]FDG signals, i.e., within-individual MC (wi-MC), as for resting-state fMRI functional connectivity (FC). The validity and interpretability of both approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel wi-MC methodology; 2) compare ai-MC maps from standardized uptake value ratio (SUVR) vs. [18F]FDG kinetic parameters fully describing the tracer behavior (i.e., Ki, K1, k3); 3) assess MC interpretability in comparison to structural connectivity and FC. We developed a new approach based on Euclidean distance to calculate wi-MC from PET time-activity curves. The across-individual correlation of SUVR, Ki, K1, k3 produced different networks depending on the chosen [18F]FDG parameter (k3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47-0.63) than for ai-MC (0.24-0.39). Our analyses demonstrate that calculating individual-level MC from dynamic PET is feasible and yields interpretable matrices that bear similarity to fMRI FC measures.
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Affiliation(s)
- Tommaso Volpi
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Giulia Vallini
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Erica Silvestri
- Department of Information Engineering, University of Padova, Padova, Italy
| | | | - Tony Durbin
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Maurizio Corbetta
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Neuroscience, University of Padova, Padova, Italy
| | - John J Lee
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Andrei G Vlassenko
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Manu S Goyal
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Alessandra Bertoldo
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Information Engineering, University of Padova, Padova, Italy
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49
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Baracchini G, Zhou Y, da Silva Castanheira J, Hansen JY, Rieck J, Turner GR, Grady CL, Misic B, Nomi J, Uddin LQ, Spreng RN. The biological role of local and global fMRI BOLD signal variability in human brain organization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.22.563476. [PMID: 37961684 PMCID: PMC10634715 DOI: 10.1101/2023.10.22.563476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Variability drives the organization and behavior of complex systems, including the human brain. Understanding the variability of brain signals is thus necessary to broaden our window into brain function and behavior. Few empirical investigations of macroscale brain signal variability have yet been undertaken, given the difficulty in separating biological sources of variance from artefactual noise. Here, we characterize the temporal variability of the most predominant macroscale brain signal, the fMRI BOLD signal, and systematically investigate its statistical, topographical and neurobiological properties. We contrast fMRI acquisition protocols, and integrate across histology, microstructure, transcriptomics, neurotransmitter receptor and metabolic data, fMRI static connectivity, and empirical and simulated magnetoencephalography data. We show that BOLD signal variability represents a spatially heterogeneous, central property of multi-scale multi-modal brain organization, distinct from noise. Our work establishes the biological relevance of BOLD signal variability and provides a lens on brain stochasticity across spatial and temporal scales.
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Affiliation(s)
- Giulia Baracchini
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Yigu Zhou
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jason da Silva Castanheira
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Justine Y. Hansen
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | | | - Gary R. Turner
- Department of Psychology, York University, Toronto, ON, Canada
| | - Cheryl L. Grady
- Rotman Research Institute at Baycrest, and Department of Psychiatry and Psychology, University of Toronto, Toronto, ON, Canada
| | - Bratislav Misic
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jason Nomi
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, USA
| | - Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, USA
| | - R. Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
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50
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Siddiqui M, Pinti P, Brigadoi S, Lloyd-Fox S, Elwell CE, Johnson MH, Tachtsidis I, Jones EJH. Using multi-modal neuroimaging to characterise social brain specialisation in infants. eLife 2023; 12:e84122. [PMID: 37818944 PMCID: PMC10624424 DOI: 10.7554/elife.84122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 10/10/2023] [Indexed: 10/13/2023] Open
Abstract
The specialised regional functionality of the mature human cortex partly emerges through experience-dependent specialisation during early development. Our existing understanding of functional specialisation in the infant brain is based on evidence from unitary imaging modalities and has thus focused on isolated estimates of spatial or temporal selectivity of neural or haemodynamic activation, giving an incomplete picture. We speculate that functional specialisation will be underpinned by better coordinated haemodynamic and metabolic changes in a broadly orchestrated physiological response. To enable researchers to track this process through development, we develop new tools that allow the simultaneous measurement of coordinated neural activity (EEG), metabolic rate, and oxygenated blood supply (broadband near-infrared spectroscopy) in the awake infant. In 4- to 7-month-old infants, we use these new tools to show that social processing is accompanied by spatially and temporally specific increases in coupled activation in the temporal-parietal junction, a core hub region of the adult social brain. During non-social processing, coupled activation decreased in the same region, indicating specificity to social processing. Coupling was strongest with high-frequency brain activity (beta and gamma), consistent with the greater energetic requirements and more localised action of high-frequency brain activity. The development of simultaneous multimodal neural measures will enable future researchers to open new vistas in understanding functional specialisation of the brain.
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Affiliation(s)
- Maheen Siddiqui
- Centre for Brain and Cognitive Development, Birkbeck, University of LondonLondonUnited Kingdom
| | - Paola Pinti
- Centre for Brain and Cognitive Development, Birkbeck, University of LondonLondonUnited Kingdom
| | - Sabrina Brigadoi
- Department of Development and Social Psychology, University of PadovaPadovaItaly
- Department of Information Engineering, University of PadovaPadovaItaly
| | - Sarah Lloyd-Fox
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Clare E Elwell
- Department of Medical Physics and Biomedical Engineering, University College LondonLondonUnited Kingdom
| | - Mark H Johnson
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Ilias Tachtsidis
- Department of Medical Physics and Biomedical Engineering, University College LondonLondonUnited Kingdom
| | - Emily JH Jones
- Centre for Brain and Cognitive Development, Birkbeck, University of LondonLondonUnited Kingdom
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