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Ruffle JK, Watkins H, Gray RJ, Hyare H, Thiebaut de Schotten M, Nachev P. Compressed representation of brain genetic transcription. Hum Brain Mapp 2024; 45:e26795. [PMID: 39045881 PMCID: PMC11267301 DOI: 10.1002/hbm.26795] [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: 02/01/2024] [Revised: 06/17/2024] [Accepted: 07/09/2024] [Indexed: 07/25/2024] Open
Abstract
The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. The established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorisation (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility across signalling, microstructural, and metabolic targets, drawn from large-scale open-source MRI and PET data. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.
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Affiliation(s)
- James K. Ruffle
- Queen Square Institute of Neurology, University College LondonLondonUK
| | - Henry Watkins
- Queen Square Institute of Neurology, University College LondonLondonUK
| | - Robert J. Gray
- Queen Square Institute of Neurology, University College LondonLondonUK
| | - Harpreet Hyare
- Queen Square Institute of Neurology, University College LondonLondonUK
| | - Michel Thiebaut de Schotten
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives‐UMR 5293, CNRS, CEA, University of BordeauxBordeauxFrance
- Brain Connectivity and Behaviour LaboratoryParisFrance
| | - Parashkev Nachev
- Queen Square Institute of Neurology, University College LondonLondonUK
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Rajiah R, Takahashi K, Aziz Q, Ruffle JK. Brain effect of transcutaneous vagal nerve stimulation: A meta-analysis of neuroimaging evidence. Neurogastroenterol Motil 2024; 36:e14484. [PMID: 36281057 DOI: 10.1111/nmo.14484] [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: 02/13/2022] [Revised: 06/23/2022] [Accepted: 09/12/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND Dysfunction in the autonomic nervous system is common throughout many functional gastrointestinal diseases (FGIDs) that have been historically difficult to treat. In recent years, transcutaneous vagal nerve stimulation (tVNS) has shown promise for improving FGID symptoms. However, the brain effects of tVNS remain unclear, which we investigated by neuroimaging meta-analysis. METHODS A total of 157 studies were identified, 4 of which were appropriate for inclusion, encompassing 60 healthy human participants. Using activation likelihood analysis estimation, we statistically quantified functional brain activity changes across three domains: (1) tVNS vs. null stimulation, (2) tVNS vs. sham stimulation, and (3) sham stimulation vs. null stimulation. KEY RESULTS tVNS significantly increased activity in the insula, anterior cingulate, inferior and superior frontal gyri, caudate and putamen, and reduced activity in the hippocampi, occipital fusiform gyri, temporal pole, and middle temporal gyri, when compared to null stimulation (all corrected p < 0.005). tVNS increased activity in the anterior cingulate gyrus, left thalamus, caudate, and paracingulate gyrus and reduced activity in right thalamus, posterior cingulate cortex, and temporal fusiform cortex, when compared to sham stimulation (all corrected p < 0.005). Sham stimulation significantly increased activity in the insula and reduced activity in the posterior cingulate and paracingulate gyrus (all corrected p < 0.001), when contrasted to null stimulation. CONCLUSIONS Brain effects of tVNS localize to regions associated with both physiological autonomic regulation and regions whose activity is modulated across numerous FGIDs, which may provide a neural basis for efficacy of this treatment. Functional activity differences between sham and null stimulation illustrate the importance of robust control procedures for future trials.
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Affiliation(s)
- Rebekah Rajiah
- Centre for Neuroscience and Trauma, Blizard Institute, Wingate Institute of Neurogastroenterology, Barts and the London School of Medicine & Dentistry, Queen Mary University of London, London, UK
| | - Kazuya Takahashi
- Centre for Neuroscience and Trauma, Blizard Institute, Wingate Institute of Neurogastroenterology, Barts and the London School of Medicine & Dentistry, Queen Mary University of London, London, UK
| | - Qasim Aziz
- Centre for Neuroscience and Trauma, Blizard Institute, Wingate Institute of Neurogastroenterology, Barts and the London School of Medicine & Dentistry, Queen Mary University of London, London, UK
| | - James K Ruffle
- Centre for Neuroscience and Trauma, Blizard Institute, Wingate Institute of Neurogastroenterology, Barts and the London School of Medicine & Dentistry, Queen Mary University of London, London, UK
- UCL Queen Square Institute of Neurology, London, UK
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3
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Ruffle JK, Gray RJ, Mohinta S, Pombo G, Kaul C, Hyare H, Rees G, Nachev P. Computational limits to the legibility of the imaged human brain. Neuroimage 2024; 291:120600. [PMID: 38569979 DOI: 10.1016/j.neuroimage.2024.120600] [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: 12/19/2023] [Revised: 03/08/2024] [Accepted: 03/31/2024] [Indexed: 04/05/2024] Open
Abstract
Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted an individual's psychology better than the coincidence of common chronic disease (p < 0.05). Serology predicted chronic disease (p < 0.05) and was best predicted by it (p < 0.001), followed by structural neuroimaging (p < 0.05). Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the human brain. We make our models and code openly available.
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Affiliation(s)
- James K Ruffle
- Queen Square Institute of Neurology, University College London, London, United Kingdom.
| | - Robert J Gray
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Samia Mohinta
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Guilherme Pombo
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Chaitanya Kaul
- School of Computing Science, University of Glasgow, Glasgow, United Kingdom
| | - Harpreet Hyare
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Geraint Rees
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Parashkev Nachev
- Queen Square Institute of Neurology, University College London, London, United Kingdom.
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4
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Dimova V, Welte-Jzyk C, Kronfeld A, Korczynski O, Baier B, Koirala N, Steenken L, Kollmann B, Tüscher O, Brockmann MA, Birklein F, Muthuraman M. Brain connectivity networks underlying resting heart rate variability in acute ischemic stroke. Neuroimage Clin 2023; 41:103558. [PMID: 38142520 PMCID: PMC10788522 DOI: 10.1016/j.nicl.2023.103558] [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: 09/14/2023] [Revised: 12/18/2023] [Accepted: 12/18/2023] [Indexed: 12/26/2023]
Abstract
Acute strokes can affect heart rate variability (HRV), the mechanisms how are not well understood. We included 42 acute stroke patients (2-7 days after ischemic stroke, mean age 66 years, 16 women). For analysis of HRV, 20 matched controls (mean age 60.7, 10 women) were recruited. HRV was assessed at rest, in a supine position and individual breathing rhythmus for 5 min. The coefficient of variation (VC), the root mean square of successive differences (RMSSD), the powers of low (LF, 0.04-0.14 Hz) and high (HF, 0.15-0.50 Hz) frequency bands were extracted. HRV parameters were z-transformed related to age- and sex-matched normal subjects. Z-values < -1 indicate reduced HRV. Acute stroke lesions were marked on diffusion-weighted images employing MRIcroN and co-registered to a T1-weighted structural volume-dataset. Using independent component analysis (ICA), stroke lesions were related to HRV. Subsequently, we used the ICA-derived lesion pattern as a seed and estimated the connectivity between these brain regions and seven common functional networks, which were obtained from 50 age-matched healthy subjects (mean age 68.9, 27 women). Especially, LF and VC were frequently reduced in patients. ICA revealed one covarying lesion pattern for LF and one similar for VC, predominantly affecting the right hemisphere. Activity in brain areas corresponding to these lesions mainly impact on limbic (r = 0.55 ± 0.08) and salience ventral attention networks (0.61 ± 0.10) in the group with reduced LF power (z-score < -1), but on control and default mode networks in the group with physiological LF power (z-score > -1). No different connectivity could be found for the respective VC groups. Our results suggest that HRV alteration after acute stroke might be due to affecting resting-state brain networks.
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Affiliation(s)
- Violeta Dimova
- Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Claudia Welte-Jzyk
- Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Andrea Kronfeld
- Department of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Oliver Korczynski
- Department of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Bernhard Baier
- Edith-Stein Fachklinik for Neurorehabilitation, Bad Bergzabern, Germany
| | - Nabin Koirala
- Haskins Laboratories, Yale University, New Haven, CT 06511, USA
| | - Livia Steenken
- Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Bianca Kollmann
- Leibniz Institute for Resilience Research (LIR) gGmbH, Mainz, Germany; Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Oliver Tüscher
- Leibniz Institute for Resilience Research (LIR) gGmbH, Mainz, Germany; Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany; Institute for Molecular Biology (IMB), Mainz, Germany
| | - Marc A Brockmann
- Department of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Frank Birklein
- Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany; Neural Engineering with Signal Analytics and Artificial Intelligence, Department of Neurology, University of Würzburg, Würzburg, Germany.
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Ruffle JK, Mohinta S, Pombo G, Gray R, Kopanitsa V, Lee F, Brandner S, Hyare H, Nachev P. Brain tumour genetic network signatures of survival. Brain 2023; 146:4736-4754. [PMID: 37665980 PMCID: PMC10629773 DOI: 10.1093/brain/awad199] [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: 02/21/2023] [Revised: 05/12/2023] [Accepted: 05/30/2023] [Indexed: 09/06/2023] Open
Abstract
Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology. Gliomas are characterized by distinct combinations of genetic and epigenetic alterations, resulting in complex interactions across multiple molecular pathways. Predicting disease evolution and prescribing individually optimal treatment requires statistical models complex enough to capture the intricate (epi)genetic structure underpinning oncogenesis. Here, we formalize this task as the inference of distinct patterns of connectivity within hierarchical latent representations of genetic networks. Evaluating multi-institutional clinical, genetic and outcome data from 4023 glioma patients over 14 years, across 12 countries, we employ Bayesian generative stochastic block modelling to reveal a hierarchical network structure of tumour genetics spanning molecularly confirmed glioblastoma, IDH-wildtype; oligodendroglioma, IDH-mutant and 1p/19q codeleted; and astrocytoma, IDH-mutant. Our findings illuminate the complex dependence between features across the genetic landscape of brain tumours and show that generative network models reveal distinct signatures of survival with better prognostic fidelity than current gold standard diagnostic categories.
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Affiliation(s)
- James K Ruffle
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Samia Mohinta
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Guilherme Pombo
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Robert Gray
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Valeriya Kopanitsa
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Faith Lee
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Sebastian Brandner
- Division of Neuropathology and Department of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Harpreet Hyare
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Parashkev Nachev
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
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6
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Gao Y, Wang S, Xin H, Feng M, Zhang Q, Sui C, Guo L, Liang C, Wen H. Disrupted Gray Matter Networks Associated with Cognitive Dysfunction in Cerebral Small Vessel Disease. Brain Sci 2023; 13:1359. [PMID: 37891728 PMCID: PMC10605932 DOI: 10.3390/brainsci13101359] [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: 08/16/2023] [Revised: 09/15/2023] [Accepted: 09/20/2023] [Indexed: 10/29/2023] Open
Abstract
This study aims to investigate the disrupted topological organization of gray matter (GM) structural networks in cerebral small vessel disease (CSVD) patients with cerebral microbleeds (CMBs). Subject-wise structural networks were constructed from GM volumetric features of 49 CSVD patients with CMBs (CSVD-c), 121 CSVD patients without CMBs (CSVD-n), and 74 healthy controls. The study used graph theory to analyze the global and regional properties of the network and their correlation with cognitive performance. We found that both the control and CSVD groups exhibited efficient small-world organization in GM networks. However, compared to controls, CSVD-c and CSVD-n patients exhibited increased global and local efficiency (Eglob/Eloc) and decreased shortest path lengths (Lp), indicating increased global integration and local specialization in structural networks. Although there was no significant global topology change, partially reorganized hub distributions were found between CSVD-c and CSVD-n patients. Importantly, regional topology in nonhub regions was significantly altered between CSVD-c and CSVD-n patients, including the bilateral anterior cingulate gyrus, left superior parietal gyrus, dorsolateral superior frontal gyrus, and right MTG, which are involved in the default mode network (DMN) and sensorimotor functional modules. Intriguingly, the global metrics (Eglob, Eloc, and Lp) were significantly correlated with MoCA, AVLT, and SCWT scores in the control group but not in the CSVD-c and CSVD-n groups. In contrast, the global metrics were significantly correlated with the SDMT score in the CSVD-s and CSVD-n groups but not in the control group. Patients with CSVD show a disrupted balance between local specialization and global integration in their GM structural networks. The altered regional topology between CSVD-c and CSVD-n patients may be due to different etiological contributions, which may offer a novel understanding of the neurobiological processes involved in CSVD with CMBs.
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Affiliation(s)
- Yian Gao
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China; (Y.G.); (C.S.)
| | - Shengpei Wang
- Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100040, China;
- University of Chinese Academy of Sciences, Beijing 101408, China
| | - Haotian Xin
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No. 45 Chang-Chun St., Xicheng District, Beijing 100054, China; (H.X.); (M.F.)
| | - Mengmeng Feng
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No. 45 Chang-Chun St., Xicheng District, Beijing 100054, China; (H.X.); (M.F.)
| | - Qihao Zhang
- Department of Radiology, Weill Cornell Medical College, New York. 407 East 61st Street, New York, NY 10044, USA;
| | - Chaofan Sui
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China; (Y.G.); (C.S.)
| | - Lingfei Guo
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China; (Y.G.); (C.S.)
| | - Changhu Liang
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jing-Wu Road No. 324, Jinan 250021, China
| | - Hongwei Wen
- Key Laboratory of Cognition and Personality (Ministry of Education), Faculty of Psychology, Southwest University, Chongqing 400715, China
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Genetics of Neurogenic Orthostatic Hypotension in Parkinson’s Disease, Results from a Cross-Sectional In Silico Study. Brain Sci 2023; 13:brainsci13030506. [PMID: 36979316 PMCID: PMC10046202 DOI: 10.3390/brainsci13030506] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/09/2023] [Accepted: 03/15/2023] [Indexed: 03/19/2023] Open
Abstract
The genetic basis of Neurogenic Orthostatic Hypotension (NOH) in Parkinson’s disease (PD) has been inadequately explored. In a cross-sectional study, we examined the association between NOH and PD-related single-nucleotide polymorphisms (SNPs) and mapped their effects on gene expression and metabolic and signaling pathways. Patients with PD, free from pathological conditions associated with OH, and not taking OH-associated medications were included. NOH was defined as per international guidelines. Logistic regression was used to relate SNPs to NOH. Linkage-disequilibrium analysis, expression quantitative trait loci, and enrichment analysis were used to assess the effects on gene expression and metabolic/signaling pathways. We included 304 PD patients in the study, 35 of whom had NOH (11.5%). NOH was more frequent in patients with SNPs in SNCA, TMEM175, FAM47E-STBD1, CCDC62, SCN3A, MIR4696, SH3GL2, and LZTS3/DDRGK1 and less frequent in those with SNPs in ITGA8, IP6K2, SIPA1L2, NDUFAF2. These SNPs affected gene expression associated with the significant hierarchical central structures of the autonomic nervous system. They influenced several metabolic/signaling pathways, most notably IP3/Ca++ signaling, the PKA-CREB pathway, and the metabolism of fatty acids. These findings provide new insights into the pathophysiology of NOH in PD and may provide targets for future therapies.
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Cipolotti L, Ruffle JK, Mole J, Xu T, Hyare H, Shallice T, Chan E, Nachev P. Graph lesion-deficit mapping of fluid intelligence. Brain 2022; 146:167-181. [PMID: 36574957 PMCID: PMC9825598 DOI: 10.1093/brain/awac304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 05/27/2022] [Accepted: 08/11/2022] [Indexed: 12/29/2022] Open
Abstract
Fluid intelligence is arguably the defining feature of human cognition. Yet the nature of its relationship with the brain remains a contentious topic. Influential proposals drawing primarily on functional imaging data have implicated 'multiple demand' frontoparietal and more widely distributed cortical networks, but extant lesion-deficit studies with greater causal power are almost all small, methodologically constrained, and inconclusive. The task demands large samples of patients, comprehensive investigation of performance, fine-grained anatomical mapping, and robust lesion-deficit inference, yet to be brought to bear on it. We assessed 165 healthy controls and 227 frontal or non-frontal patients with unilateral brain lesions on the best-established test of fluid intelligence, Raven's Advanced Progressive Matrices, employing an array of lesion-deficit inferential models responsive to the potentially distributed nature of fluid intelligence. Non-parametric Bayesian stochastic block models were used to reveal the community structure of lesion deficit networks, disentangling functional from confounding pathological distributed effects. Impaired performance was confined to patients with frontal lesions [F(2,387) = 18.491; P < 0.001; frontal worse than non-frontal and healthy participants P < 0.01, P <0.001], more marked on the right than left [F(4,385) = 12.237; P < 0.001; right worse than left and healthy participants P < 0.01, P < 0.001]. Patients with non-frontal lesions were indistinguishable from controls and showed no modulation by laterality. Neither the presence nor the extent of multiple demand network involvement affected performance. Both conventional network-based statistics and non-parametric Bayesian stochastic block modelling heavily implicated the right frontal lobe. Crucially, this localization was confirmed on explicitly disentangling functional from pathology-driven effects within a layered stochastic block model, prominently highlighting a right frontal network involving middle and inferior frontal gyrus, pre- and post-central gyri, with a weak contribution from right superior parietal lobule. Similar results were obtained with standard lesion-deficit analyses. Our study represents the first large-scale investigation of the distributed neural substrates of fluid intelligence in the focally injured brain. Combining novel graph-based lesion-deficit mapping with detailed investigation of cognitive performance in a large sample of patients provides crucial information about the neural basis of intelligence. Our findings indicate that a set of predominantly right frontal regions, rather than a more widely distributed network, is critical to the high-level functions involved in fluid intelligence. Further they suggest that Raven's Advanced Progressive Matrices is a useful clinical index of fluid intelligence and a sensitive marker of right frontal lobe dysfunction.
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Affiliation(s)
- Lisa Cipolotti
- Correspondence to: Prof. Lisa Cipolotti Department of NeuropsychologyNational Hospital for Neurology and NeurosurgeryQueen Square, London WC1N 3BG, UKE-mail:
| | - James K Ruffle
- Institute of Neurology, University College London, London WC1N 3BG, UK,Department of Radiology, University College London Hospitals NHS Foundation Trust, London NW1 2PG, UK
| | - Joe Mole
- Department of Neuropsychology, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK,Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Tianbo Xu
- Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Harpreet Hyare
- Institute of Neurology, University College London, London WC1N 3BG, UK,Department of Radiology, University College London Hospitals NHS Foundation Trust, London NW1 2PG, UK
| | - Tim Shallice
- Institute of Cognitive Neuroscience, University College London, London WC1N 3AZ, UK,Cognitive Neuropsychology and Neuroimaging Lab, International School for Advanced Studies (SISSA-ISAS), 34136 Trieste, Italy
| | - Edgar Chan
- Department of Neuropsychology, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK,Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Parashkev Nachev
- Institute of Neurology, University College London, London WC1N 3BG, UK
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Ferraro S, Klugah-Brown B, Tench CR, Bazinet V, Bore MC, Nigri A, Demichelis G, Bruzzone MG, Palermo S, Zhao W, Yao S, Jiang X, Kendrick KM, Becker B. The central autonomic system revisited – Convergent evidence for a regulatory role of the insular and midcingulate cortex from neuroimaging meta-analyses. Neurosci Biobehav Rev 2022; 142:104915. [DOI: 10.1016/j.neubiorev.2022.104915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/28/2022] [Accepted: 10/09/2022] [Indexed: 11/17/2022]
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10
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Kilpatrick LA, Siddarth P, Milillo MM, Krause-Sorio B, Ercoli L, Narr KL, Lavretsky H. Impact of Tai Chi as an adjunct treatment on brain connectivity in geriatric depression. J Affect Disord 2022; 315:1-6. [PMID: 35905792 PMCID: PMC10182814 DOI: 10.1016/j.jad.2022.07.049] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/06/2022] [Accepted: 07/20/2022] [Indexed: 10/16/2022]
Abstract
BACKGROUND As an adjunct to antidepressant treatment, Tai Chi Chih (TCC) is superior to health education and wellness (HEW) training in improving the general health of patients with geriatric depression (GD). This study investigated the brain connectivity changes associated with TCC and HEW in combination with antidepressant treatment in patients with GD. METHODS Forty patients with GD under stable antidepressant treatment underwent TCC training (n = 21) or HEW training (n = 19) for 12 weeks, and completed baseline and 3-month follow-up resting state magnetic resonance imaging scans. Within-group and between-group differences in parcel-to-parcel connectivity changes with intervention were evaluated by general linear modeling. Relationships between significant connectivity changes and symptom/resilience improvement were evaluated by partial least squares correlation analysis. RESULTS Significantly greater increases in connectivity with TCC than with HEW (FDR-corrected p < .05) were observed for 167 pairwise connections, most frequently involving the default mode network (DMN). In both groups, increased connectivity involving largely DMN regions was significantly and positively correlated with improvement in symptoms/resilience. LIMITATIONS The sample size was relatively small, mainly due to neuroimaging contraindications (e.g., implants). Additionally, the standard antidepressant treatment varied greatly among patients, adding heterogeneity. CONCLUSIONS Non-pharmacological adjuncts, such as TCC, may enhance DMN connectivity changes associated with improved depressive symptoms and psychological resilience in the treatment of GD.
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Affiliation(s)
- Lisa A Kilpatrick
- Department of Psychiatry, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA; G. Oppenheimer Center for Neurobiology of Stress and Resilience, University of California, Los Angeles, CA, USA
| | - Prabha Siddarth
- Department of Psychiatry, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Michaela M Milillo
- Department of Psychiatry, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Beatrix Krause-Sorio
- Department of Psychiatry, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Linda Ercoli
- Department of Psychiatry, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Katherine L Narr
- Department of Neurology, Brain Research Institute, University of California, Los Angeles, CA, USA
| | - Helen Lavretsky
- Department of Psychiatry, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA.
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Differentiating central nervous system infection from disease infiltration in hematological malignancy. Sci Rep 2022; 12:15805. [PMID: 36138051 PMCID: PMC9499957 DOI: 10.1038/s41598-022-19769-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 09/05/2022] [Indexed: 11/24/2022] Open
Abstract
Hematological malignancies place individuals at risk of CNS involvement from their hematological disease and opportunistic intracranial infection secondary to disease-/treatment-associated immunosuppression. Differentiating CNS infection from hematological disease infiltration in these patients is valuable but often challenging. We sought to determine if statistical models might aid discrimination between these processes. Neuroradiology, clinical and laboratory data for patients with hematological malignancy at our institution between 2007 and 2017 were retrieved. MRI were deep-phenotyped across anatomical distribution, presence of pathological enhancement, diffusion restriction and hemorrhage and statistically modelled with Bayesian-directed probability networks and multivariate logistic regression. 109 patients were studied. Irrespective of a diagnosis of CNS infection or hematological disease, the commonest anatomical distributions of abnormality were multifocal-parenchymal (34.9%), focal-parenchymal (29.4%) and leptomeningeal (11.9%). Pathological enhancement was the most frequently observed abnormality (46.8%), followed by hemorrhage (22.9%) and restricted diffusion (19.3%). Logistic regression could differentiate CNS infection from hematological disease infiltration with an AUC of 0.85 where, with OR > 1 favoring CNS infection and < 1 favoring CNS hematological disease, significantly predictive imaging features were hemorrhage (OR 24.61, p = 0.02), pathological enhancement (OR 0.17, p = 0.04) and an extra-axial location (OR 0.06, p = 0.05). In conclusion, CNS infection and hematological disease are heterogeneous entities with overlapping radiological appearances but a multivariate interaction of MR imaging features may assist in distinguishing them.
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Wang R, Köhrmann M, Kollmar R, Koehn J, Schwab S, Kallmünzer B, Hilz MJ. Posterior circulation ischemic stroke not involving the brainstem is associated with cardiovascular autonomic dysfunction. Eur J Neurol 2022; 29:2690-2700. [PMID: 35638371 DOI: 10.1111/ene.15427] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 05/23/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND PURPOSE Ischemic stroke may induce cardiovascular autonomic dysfunction. Yet, most previous studies included patients with anterior circulation ischemic stroke or brainstem stroke. It remains unclear whether posterior circulation ischemic stroke (PCIS) without brainstem involvement also compromises cardiovascular autonomic modulation (CAM). Therefore, we aimed to assess CAM in PCIS patients with and without brainstem involvement. METHODS In four subgroups of 61 PCIS-patients (14 occipital lobe, 16 thalamic, 12 cerebellar, and 19 brainstem strokes) and 30 healthy controls, we recorded RR-intervals (RRI), systolic, diastolic blood pressures (BPsys, BPdia), and respiration at supine rest during the first week after stroke-onset. We calculated parameters reflecting total CAM [RRI-standard-deviation (RRI-SD), RRI-total-powers], predominantly sympathetic CAM [RRI-low-frequency-powers (RRI-LF-powers) and BPsys-LF-powers] and parasympathetic CAM [Root-Mean-Square-of-Successive-RRI-Differences (RMSSD), RRI-high-frequency-powers (RRI-HF-powers)], sympathetic-parasympathetic balance (RRI-LF/HF-ratios), and baroreflex-sensitivity (BRS). Values were compared between the four PCIS-groups and controls using one-way ANOVA Kruskal-Wallis-tests, with post-hoc analyses. Significance was assumed for P<0.05. RESULTS In each PCIS-subgroup, values of RRI, RRI-SD, RMSSD, RRI-HF-powers, and BRS were significantly lower, while BPsys-LF-powers were higher than in the controls. Only in patients with occipital lobe stroke, RRI-LF/HF-ratios were significantly higher than in controls. Otherwise, autonomic parameters did not differ between the four PCIS-subgroups. CONCLUSIONS During the first week after stroke-onset, our PCIS patients with occipital lobe, thalamic, cerebellar, or brainstem strokes all had reduced cardiovagal modulation, compromised baroreflex, and increased peripheral sympathetic modulation. The RRI-LF/HF-ratios suggest that sympathetic predominance is slightly more prominent after occipital lobe stroke. PCIS may trigger cardiovascular autonomic dysfunction even without brainstem involvement.
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Affiliation(s)
- Ruihao Wang
- Department of Neurology, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Martin Köhrmann
- Department of Neurology, University Hospital Essen, Essen, Germany
| | - Rainer Kollmar
- Department of Neurology, General Hospital Darmstadt, Darmstadt, Germany
| | - Julia Koehn
- Department of Neurology, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Stefan Schwab
- Department of Neurology, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Bernd Kallmünzer
- Department of Neurology, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Max J Hilz
- Department of Neurology, University of Erlangen-Nuremberg, Erlangen, Germany.,Icahn School of Medicine at Mount Sinai, New York, NY, USA
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