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Nolan E, Loh KK, Petrides M. Morphological patterns and spatial probability maps of the inferior frontal sulcus in the human brain. Hum Brain Mapp 2024; 45:e26759. [PMID: 38989632 PMCID: PMC11237881 DOI: 10.1002/hbm.26759] [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: 03/03/2024] [Revised: 05/07/2024] [Accepted: 05/31/2024] [Indexed: 07/12/2024] Open
Abstract
The inferior frontal sulcus (ifs) is a prominent sulcus on the lateral frontal cortex, separating the middle frontal gyrus from the inferior frontal gyrus. The morphology of the ifs can be difficult to distinguish from adjacent sulci, which are often misidentified as continuations of the ifs. The morphological variability of the ifs and its relationship to surrounding sulci were examined in 40 healthy human subjects (i.e., 80 hemispheres). The sulci were identified and labeled on the native cortical surface meshes of individual subjects, permitting proper intra-sulcal assessment. Two main morphological patterns of the ifs were identified across hemispheres: in Type I, the ifs was a single continuous sulcus, and in Type II, the ifs was discontinuous and appeared in two segments. The morphology of the ifs could be further subdivided into nine subtypes based on the presence of anterior and posterior sulcal extensions. The ifs was often observed to connect, either superficially or completely, with surrounding sulci, and seldom appeared as an independent sulcus. The spatial variability of the ifs and its various morphological configurations were quantified in the form of surface spatial probability maps which are made publicly available in the standard fsaverage space. These maps demonstrated that the ifs generally occupied a consistent position across hemispheres and across individuals. The normalized mean sulcal depths associated with the main morphological types were also computed. The present study provides the first detailed description of the ifs as a sulcal complex composed of segments and extensions that can be clearly differentiated from adjacent sulci. These descriptions, together with the spatial probability maps, are critical for the accurate identification of the ifs in anatomical and functional neuroimaging studies investigating the structural characteristics and functional organization of this region in the human brain.
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Affiliation(s)
- Erika Nolan
- Department of Psychology, McGill University, Montreal, Canada
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Kep Kee Loh
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada
- Department of Psychology, National University of Singapore, Singapore
| | - Michael Petrides
- Department of Psychology, McGill University, Montreal, Canada
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada
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2
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Du J, DiNicola LM, Angeli PA, Saadon-Grosman N, Sun W, Kaiser S, Ladopoulou J, Xue A, Yeo BTT, Eldaief MC, Buckner RL. Organization of the human cerebral cortex estimated within individuals: networks, global topography, and function. J Neurophysiol 2024; 131:1014-1082. [PMID: 38489238 DOI: 10.1152/jn.00308.2023] [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: 08/16/2023] [Revised: 01/18/2024] [Accepted: 02/16/2024] [Indexed: 03/17/2024] Open
Abstract
The cerebral cortex is populated by specialized regions that are organized into networks. Here we estimated networks from functional MRI (fMRI) data in intensively sampled participants. The procedure was developed in two participants (scanned 31 times) and then prospectively applied to 15 participants (scanned 8-11 times). Analysis of the networks revealed a global organization. Locally organized first-order sensory and motor networks were surrounded by spatially adjacent second-order networks that linked to distant regions. Third-order networks possessed regions distributed widely throughout association cortex. Regions of distinct third-order networks displayed side-by-side juxtapositions with a pattern that repeated across multiple cortical zones. We refer to these as supra-areal association megaclusters (SAAMs). Within each SAAM, two candidate control regions were adjacent to three separate domain-specialized regions. Response properties were explored with task data. The somatomotor and visual networks responded to body movements and visual stimulation, respectively. Second-order networks responded to transients in an oddball detection task, consistent with a role in orienting to salient events. The third-order networks, including distinct regions within each SAAM, showed two levels of functional specialization. Regions linked to candidate control networks responded to working memory load across multiple stimulus domains. The remaining regions dissociated across language, social, and spatial/episodic processing domains. These results suggest that progressively higher-order networks nest outward from primary sensory and motor cortices. Within the apex zones of association cortex, there is specialization that repeatedly divides domain-flexible from domain-specialized regions. We discuss implications of these findings, including how repeating organizational motifs may emerge during development.NEW & NOTEWORTHY The organization of cerebral networks was estimated within individuals with intensive, repeat sampling of fMRI data. A hierarchical organization emerged in each individual that delineated first-, second-, and third-order cortical networks. Regions of distinct third-order association networks consistently exhibited side-by-side juxtapositions that repeated across multiple cortical zones, with clear and robust functional specialization among the embedded regions.
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Affiliation(s)
- Jingnan Du
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Lauren M DiNicola
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Peter A Angeli
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Noam Saadon-Grosman
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Wendy Sun
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Stephanie Kaiser
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Joanna Ladopoulou
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Aihuiping Xue
- Centre for Sleep & Cognition and Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - B T Thomas Yeo
- Centre for Sleep & Cognition and Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Mark C Eldaief
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
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3
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Lapate RC, Heckner MK, Phan AT, Tambini A, D'Esposito M. Information-based TMS to mid-lateral prefrontal cortex disrupts action goals during emotional processing. Nat Commun 2024; 15:4294. [PMID: 38769359 PMCID: PMC11106324 DOI: 10.1038/s41467-024-48015-8] [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: 06/26/2023] [Accepted: 04/18/2024] [Indexed: 05/22/2024] Open
Abstract
The ability to respond to emotional events in a context-sensitive and goal-oriented manner is essential for adaptive functioning. In models of behavioral and emotion regulation, the lateral prefrontal cortex (LPFC) is postulated to maintain goal-relevant representations that promote cognitive control, an idea rarely tested with causal inference. Here, we altered mid-LPFC function in healthy individuals using a putatively inhibitory brain stimulation protocol (continuous theta burst; cTBS), followed by fMRI scanning. Participants performed the Affective Go/No-Go task, which requires goal-oriented action during affective processing. We targeted mid-LPFC (vs. a Control site) based on the individualized location of action-goal representations observed during the task. cTBS to mid-LPFC reduced action-goal representations in mid-LPFC and impaired goal-oriented action, particularly during processing of negative emotional cues. During negative-cue processing, cTBS to mid-LPFC reduced functional coupling between mid-LPFC and nodes of the default mode network, including frontopolar cortex-a region thought to modulate LPFC control signals according to internal states. Collectively, these results indicate that mid-LPFC goal-relevant representations play a causal role in governing context-sensitive cognitive control during emotional processing.
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Affiliation(s)
- R C Lapate
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA.
| | - M K Heckner
- Institute of Neuroscience and Medicine, Research Centre Jülich, Jülich, Germany
| | - A T Phan
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA
| | - A Tambini
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Department of Psychiatry, New York University School of Medicine, New York, NY, USA
| | - M D'Esposito
- Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
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4
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Surgent O, Guerrero-Gonzalez J, Dean DC, Adluru N, Kirk GR, Kecskemeti SR, Alexander AL, Li JJ, Travers BG. Microstructural neural correlates of maximal grip strength in autistic children: the role of the cortico-cerebellar network and attention-deficit/hyperactivity disorder features. Front Integr Neurosci 2024; 18:1359099. [PMID: 38808069 PMCID: PMC11130426 DOI: 10.3389/fnint.2024.1359099] [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: 12/20/2023] [Accepted: 04/24/2024] [Indexed: 05/30/2024] Open
Abstract
Introduction Maximal grip strength, a measure of how much force a person's hand can generate when squeezing an object, may be an effective method for understanding potential neurobiological differences during motor tasks. Grip strength in autistic individuals may be of particular interest due to its unique developmental trajectory. While autism-specific differences in grip-brain relationships have been found in adult populations, it is possible that such differences in grip-brain relationships may be present at earlier ages when grip strength is behaviorally similar in autistic and non-autistic groups. Further, such neural differences may lead to the later emergence of diagnostic-group grip differences in adolescence. The present study sought to examine this possibility, while also examining if grip strength could elucidate the neuro-motor sources of phenotypic heterogeneity commonly observed within autism. Methods Using high resolution, multi-shell diffusion, and quantitative R1 relaxometry imaging, this study examined how variations in key sensorimotor-related white matter pathways of the proprioception input, lateral grasping, cortico-cerebellar, and corticospinal networks were associated with individual variations in grip strength in 68 autistic children and 70 non-autistic (neurotypical) children (6-11 years-old). Results In both groups, results indicated that stronger grip strength was associated with higher proprioceptive input, lateral grasping, and corticospinal (but not cortico-cerebellar modification) fractional anisotropy and R1, indirect measures concordant with stronger microstructural coherence and increased myelination. Diagnostic group differences in these grip-brain relationships were not observed, but the autistic group exhibited more variability particularly in the cortico-cerebellar modification indices. An examination into the variability within the autistic group revealed that attention-deficit/hyperactivity disorder (ADHD) features moderated the relationships between grip strength and both fractional anisotropy and R1 relaxometry in the premotor-primary motor tract of the lateral grasping network and the cortico-cerebellar network tracts. Specifically, in autistic children with elevated ADHD features (60% of the autistic group) stronger grip strength was related to higher fractional anisotropy and R1 of the cerebellar modification network (stronger microstructural coherence and more myelin), whereas the opposite relationship was observed in autistic children with reduced ADHD features. Discussion Together, this work suggests that while the foundational elements of grip strength are similar across school-aged autistic and non-autistic children, neural mechanisms of grip strength within autistic children may additionally depend on the presence of ADHD features. Specifically, stronger, more coherent connections of the cerebellar modification network, which is thought to play a role in refining and optimizing motor commands, may lead to stronger grip in children with more ADHD features, weaker grip in children with fewer ADHD features, and no difference in grip in non-autistic children. While future research is needed to understand if these findings extend to other motor tasks beyond grip strength, these results have implications for understanding the biological basis of neuromotor control in autistic children and emphasize the importance of assessing co-occurring conditions when evaluating brain-behavior relationships in autism.
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Affiliation(s)
- Olivia Surgent
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, United States
| | - Jose Guerrero-Gonzalez
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States
| | - Douglas C. Dean
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States
- Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, United States
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Gregory R. Kirk
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
| | | | - Andrew L. Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - James J. Li
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Psychology Department, University of Wisconsin-Madison, Madison, WI, United States
| | - Brittany G. Travers
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Occupational Therapy Program in the Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States
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5
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Santacroce F, Cachia A, Fragueiro A, Grande E, Roell M, Baldassarre A, Sestieri C, Committeri G. Human intraparietal sulcal morphology relates to individual differences in language and memory performance. Commun Biol 2024; 7:520. [PMID: 38698168 PMCID: PMC11065983 DOI: 10.1038/s42003-024-06175-9] [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: 05/19/2023] [Accepted: 04/10/2024] [Indexed: 05/05/2024] Open
Abstract
The sulco-gyral pattern is a qualitative feature of the cortical anatomy that is determined in utero, stable throughout lifespan and linked to brain function. The intraparietal sulcus (IPS) is a nodal associative brain area, but the relation between its morphology and cognition is largely unknown. By labelling the left and right IPS of 390 healthy participants into two patterns, according to the presence or absence of a sulcus interruption, here we demonstrate a strong association between the morphology of the right IPS and performance on memory and language tasks. We interpret the results as a morphological advantage of a sulcus interruption, probably due to the underlying white matter organization. The right-hemisphere specificity of this effect emphasizes the neurodevelopmental and plastic role of sulcus morphology in cognition prior to lateralisation processes. The results highlight a promising area of investigation on the relationship between cognitive performance, sulco-gyral pattern and white matter bundles.
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Affiliation(s)
- Federica Santacroce
- Department of Neuroscience, Imaging and Clinical Sciences, and ITAB, Gabriele d'Annunzio University, Via Luigi Polacchi 11, 66100, Chieti, Italy.
| | - Arnaud Cachia
- Université Paris Cité, Laboratoire de Psychologie du développement et de l'Education de l'Enfant (LaPsyDÉ), CNRS UMR 8240, Paris, France
- Université Paris Cité, Institut de Psychiatrie et Neurosciences de Paris (IPNP), INSERM, UMR S1266, Paris, France
| | - Agustina Fragueiro
- Department of Neuroscience, Imaging and Clinical Sciences, and ITAB, Gabriele d'Annunzio University, Via Luigi Polacchi 11, 66100, Chieti, Italy
| | - Eleonora Grande
- Department of Neuroscience, Imaging and Clinical Sciences, and ITAB, Gabriele d'Annunzio University, Via Luigi Polacchi 11, 66100, Chieti, Italy
| | - Margot Roell
- Université Paris Cité, Laboratoire de Psychologie du développement et de l'Education de l'Enfant (LaPsyDÉ), CNRS UMR 8240, Paris, France
| | - Antonello Baldassarre
- Department of Neuroscience, Imaging and Clinical Sciences, and ITAB, Gabriele d'Annunzio University, Via Luigi Polacchi 11, 66100, Chieti, Italy
| | - Carlo Sestieri
- Department of Neuroscience, Imaging and Clinical Sciences, and ITAB, Gabriele d'Annunzio University, Via Luigi Polacchi 11, 66100, Chieti, Italy
| | - Giorgia Committeri
- Department of Neuroscience, Imaging and Clinical Sciences, and ITAB, Gabriele d'Annunzio University, Via Luigi Polacchi 11, 66100, Chieti, Italy.
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6
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Cobia D, Haut MW, Revill KP, Rellick SL, Nudo RJ, Wischnewski M, Buetefisch CM. Gray matter volume of functionally relevant primary motor cortex is causally related to learning a hand motor task. Cereb Cortex 2024; 34:bhae210. [PMID: 38771243 PMCID: PMC11107379 DOI: 10.1093/cercor/bhae210] [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: 01/31/2024] [Revised: 04/24/2024] [Accepted: 04/28/2024] [Indexed: 05/22/2024] Open
Abstract
Variability in brain structure is associated with the capacity for behavioral change. However, a causal link between specific brain areas and behavioral change (such as motor learning) has not been demonstrated. We hypothesized that greater gray matter volume of a primary motor cortex (M1) area active during a hand motor learning task is positively correlated with subsequent learning of the task, and that the disruption of this area blocks learning of the task. Healthy participants underwent structural MRI before learning a skilled hand motor task. Next, participants performed this learning task during fMRI to determine M1 areas functionally active during this task. This functional ROI was anatomically constrained with M1 boundaries to create a group-level "Active-M1" ROI used to measure gray matter volume in each participant. Greater gray matter volume in the left hemisphere Active-M1 ROI was related to greater motor learning in the corresponding right hand. When M1 hand area was disrupted with repetitive transcranial stimulation (rTMS), learning of the motor task was blocked, confirming its causal link to motor learning. Our combined imaging and rTMS approach revealed greater cortical volume in a task-relevant M1 area is causally related to learning of a hand motor task in healthy humans.
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Affiliation(s)
- Derin Cobia
- Department of Psychology and Neuroscience Center, 1036 KMBL, Brigham Young University, Provo, UT 84602, USA
| | - Marc W Haut
- Department of Behavioral Medicine and Psychiatry, Rockefeller Neuroscience Institute, 33 Medical Center Dr., West Virginia University, Morgantown, WV 26506, USA
- Department of Neurology, Rockefeller Neuroscience Institute, 33 Medical Center Dr., West Virginia University, Morgantown, WV 26506, USA
- Department of Radiology, Rockefeller Neuroscience Institute, 33 Medical Center Dr., West Virginia University, Morgantown, WV 26506, USA
| | - Kate P Revill
- Department of Psychology, Emory University, 36 Eagle Row, Atlanta, GA 30322, USA
| | - Stephanie L Rellick
- Department of Behavioral Medicine and Psychiatry, Rockefeller Neuroscience Institute, 33 Medical Center Dr., West Virginia University, Morgantown, WV 26506, USA
| | - Randolph J Nudo
- Department of Rehabilitation Medicine, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
| | - Miles Wischnewski
- Department of Neurology, Emory University, 1441 Clifton Road NE, Suite 236 C, Atlanta, GA 30322, USA
| | - Cathrin M Buetefisch
- Department of Neurology, Emory University, 1441 Clifton Road NE, Suite 236 C, Atlanta, GA 30322, USA
- Department of Rehabilitation Medicine, Emory University, 1441 Clifton Road NE, Suite 236 C, Atlanta, GA 30322, USA
- Department of Radiology, Emory University, 1441 Clifton Road NE, Suite 236 C, Atlanta, GA 30322, USA
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7
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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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8
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Downey JE, Schone HR, Foldes ST, Greenspon C, Liu F, Verbaarschot C, Biro D, Satzer D, Moon CH, Coffman BA, Youssofzadeh V, Fields D, Hobbs TG, Okorokova E, Tyler-Kabara EC, Warnke PC, Gonzalez-Martinez J, Hatsopoulos NG, Bensmaia SJ, Boninger ML, Gaunt RA, Collinger JL. A roadmap for implanting microelectrode arrays to evoke tactile sensations through intracortical microstimulation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.26.24306239. [PMID: 38712177 PMCID: PMC11071570 DOI: 10.1101/2024.04.26.24306239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Intracortical microstimulation (ICMS) is a method for restoring sensation to people with paralysis as part of a bidirectional brain-computer interface to restore upper limb function. Evoking tactile sensations of the hand through ICMS requires precise targeting of implanted electrodes. Here we describe the presurgical imaging procedures used to generate functional maps of the hand area of the somatosensory cortex and subsequent planning that guided the implantation of intracortical microelectrode arrays. In five participants with cervical spinal cord injury, across two study locations, this procedure successfully enabled ICMS-evoked sensations localized to at least the first four digits of the hand. The imaging and planning procedures developed through this clinical trial provide a roadmap for other brain-computer interface studies to ensure successful placement of stimulation electrodes.
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Affiliation(s)
- John E Downey
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Hunter R Schone
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
| | - Stephen T Foldes
- Department of Neurology, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ
| | - Charles Greenspon
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Fang Liu
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
| | - Ceci Verbaarschot
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
| | - Daniel Biro
- Department of Neurological Surgery, University of Chicago, Chicago, IL
| | - David Satzer
- Department of Neurological Surgery, University of Chicago, Chicago, IL
| | - Chan Hong Moon
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA
| | - Brian A Coffman
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA
| | | | - Daryl Fields
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA
| | - Taylor G Hobbs
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
| | - Elizaveta Okorokova
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | | | - Peter C Warnke
- Department of Neurological Surgery, University of Chicago, Chicago, IL
| | | | - Nicholas G Hatsopoulos
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
- Committee on Computation Neuroscience, University of Chicago, Chicago, IL
- Neuroscience Institute, University of Chicago, Chicago, IL
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
- Committee on Computation Neuroscience, University of Chicago, Chicago, IL
- Neuroscience Institute, University of Chicago, Chicago, IL
| | - Michael L Boninger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
| | - Robert A Gaunt
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA
| | - Jennifer L Collinger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA
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9
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Lankinen K, Ahveninen J, Jas M, Raij T, Ahlfors SP. Neuronal Modeling of Cross-Sensory Visual Evoked Magnetoencephalography Responses in the Auditory Cortex. J Neurosci 2024; 44:e1119232024. [PMID: 38508715 PMCID: PMC11044114 DOI: 10.1523/jneurosci.1119-23.2024] [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: 06/16/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 03/22/2024] Open
Abstract
Previous studies have demonstrated that auditory cortex activity can be influenced by cross-sensory visual inputs. Intracortical laminar recordings in nonhuman primates have suggested a feedforward (FF) type profile for auditory evoked but feedback (FB) type for visual evoked activity in the auditory cortex. To test whether cross-sensory visual evoked activity in the auditory cortex is associated with FB inputs also in humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimated MEG source waveforms for auditory cortex regions of interest, auditory evoked response showed peaks at 37 and 90 ms and visual evoked response at 125 ms. The inputs to the auditory cortex were modeled through FF- and FB-type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which links cellular- and circuit-level mechanisms to MEG signals. HNN modeling suggested that the experimentally observed auditory response could be explained by an FF input followed by an FB input, whereas the cross-sensory visual response could be adequately explained by just an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas.
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Affiliation(s)
- Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Mainak Jas
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Seppo P Ahlfors
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
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10
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Trišins M, Zdanovskis N, Platkājis A, Šneidere K, Kostiks A, Karelis G, Stepens A. Brodmann Areas, V1 Atlas and Cognitive Impairment: Assessing Cortical Thickness for Cognitive Impairment Diagnostics. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:587. [PMID: 38674233 PMCID: PMC11052167 DOI: 10.3390/medicina60040587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/26/2024] [Accepted: 03/29/2024] [Indexed: 04/28/2024]
Abstract
Background and Objectives: Magnetic resonance imaging is vital for diagnosing cognitive decline. Brodmann areas (BA), distinct regions of the cerebral cortex categorized by cytoarchitectural variances, provide insights into cognitive function. This study aims to compare cortical thickness measurements across brain areas identified by BA mapping. We assessed these measurements among patients with and without cognitive impairment, and across groups categorized by cognitive performance levels using the Montreal Cognitive Assessment (MoCA) test. Materials and Methods: In this cross-sectional study, we included 64 patients who were divided in two ways: in two groups with (CI) or without (NCI) impaired cognitive function and in three groups with normal (NC), moderate (MPG) and low (LPG) cognitive performance according to MoCA scores. Scans with a 3T MRI scanner were carried out, and cortical thickness data was acquired using Freesurfer 7.2.0 software. Results: By analyzing differences between the NCI and CI groups cortical thickness of BA3a in left hemisphere (U = 241.000, p = 0.016), BA4a in right hemisphere (U = 269.000, p = 0.048) and BA28 in left hemisphere (U = 584.000, p = 0.005) showed significant differences. In the LPG, MPG and NC cortical thickness in BA3a in left hemisphere (H (2) = 6.268, p = 0.044), in V2 in right hemisphere (H (2) = 6.339, p = 0.042), in BA28 in left hemisphere (H (2) = 23.195, p < 0.001) and in BA28 in right hemisphere (H (2) = 10.015, p = 0.007) showed significant differences. Conclusions: Our study found that cortical thickness in specific Brodmann Areas-BA3a and BA28 in the left hemisphere, and BA4a in the right-differ significantly between NCI and CI groups. Significant differences were also observed in BA3a (left), V2 (right), and BA28 (both hemispheres) across LPG, MPG, NC groups. Despite a small sample size, these findings suggest cortical thickness measurements can serve as effective biomarkers for cognitive impairment diagnosis, warranting further validation with a larger cohort.
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Affiliation(s)
- Maksims Trišins
- Department of Radiology, Riga Stradins University, LV-1007 Riga, Latvia; (M.T.)
| | - Nauris Zdanovskis
- Department of Radiology, Riga Stradins University, LV-1007 Riga, Latvia; (M.T.)
- Department of Radiology, Riga East University Hospital, LV-1038 Riga, Latvia
- Military Medicine Research and Study Centre, Riga Stradins University, LV-1007 Riga, Latvia
| | - Ardis Platkājis
- Department of Radiology, Riga Stradins University, LV-1007 Riga, Latvia; (M.T.)
- Department of Radiology, Riga East University Hospital, LV-1038 Riga, Latvia
| | - Kristīne Šneidere
- Military Medicine Research and Study Centre, Riga Stradins University, LV-1007 Riga, Latvia
- Department of Health Psychology and Pedagogy, Riga Stradins University, LV-1007 Riga, Latvia
| | - Andrejs Kostiks
- Department of Neurology and Neurosurgery, Riga East University Hospital, LV-1038 Riga, Latvia (G.K.)
| | - Guntis Karelis
- Department of Neurology and Neurosurgery, Riga East University Hospital, LV-1038 Riga, Latvia (G.K.)
- Department of Infectology, Riga Stradins University, LV-1007 Riga, Latvia
| | - Ainārs Stepens
- Military Medicine Research and Study Centre, Riga Stradins University, LV-1007 Riga, Latvia
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11
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Cash RFH, Zalesky A. Personalized and Circuit-Based Transcranial Magnetic Stimulation: Evidence, Controversies, and Opportunities. Biol Psychiatry 2024; 95:510-522. [PMID: 38040047 DOI: 10.1016/j.biopsych.2023.11.013] [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: 06/14/2023] [Revised: 11/13/2023] [Accepted: 11/18/2023] [Indexed: 12/03/2023]
Abstract
The development of neuroimaging methodologies to map brain connectivity has transformed our understanding of psychiatric disorders, the distributed effects of brain stimulation, and how transcranial magnetic stimulation can be best employed to target and ameliorate psychiatric symptoms. In parallel, neuroimaging research has revealed that higher-order brain regions such as the prefrontal cortex, which represent the most common therapeutic brain stimulation targets for psychiatric disorders, show some of the highest levels of interindividual variation in brain connectivity. These findings provide the rationale for personalized target site selection based on person-specific brain network architecture. Recent advances have made it possible to determine reproducible personalized targets with millimeter precision in clinically tractable acquisition times. These advances enable the potential advantages of spatially personalized transcranial magnetic stimulation targeting to be evaluated and translated to basic and clinical applications. In this review, we outline the motivation for target site personalization, preliminary support (mostly in depression), convergent evidence from other brain stimulation modalities, and generalizability beyond depression and the prefrontal cortex. We end by detailing methodological recommendations, controversies, and notable alternatives. Overall, while this research area appears highly promising, the value of personalized targeting remains unclear, and dedicated large prospective randomized clinical trials using validated methodology are critical.
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Affiliation(s)
- Robin F H Cash
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, Australia.
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, Australia
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12
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Cao Z, Xiao X, Xie C, Wei L, Yang Y, Zhu C. Personalized connectivity-based network targeting model of transcranial magnetic stimulation for treatment of psychiatric disorders: computational feasibility and reproducibility. Front Psychiatry 2024; 15:1341908. [PMID: 38419897 PMCID: PMC10899497 DOI: 10.3389/fpsyt.2024.1341908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/24/2024] [Indexed: 03/02/2024] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) holds promise for treating psychiatric disorders; however, the variability in treatment efficacy among individuals underscores the need for further improvement. Growing evidence has shown that TMS induces a broad network modulatory effect, and its effectiveness may rely on accurate modulation of the pathological network specific to each disorder. Therefore, determining the optimal TMS coil setting that will engage the functional pathway delivering the stimulation is crucial. Compared to group-averaged functional connectivity (FC), individual FC provides specific information about a person's brain functional architecture, offering the potential for more accurate network targeting for personalized TMS. However, the low signal-to-noise ratio (SNR) of FC poses a challenge when utilizing individual resting-state FC. To overcome this challenge, the proposed solutions include increasing the scan duration and employing the cluster method to enhance the stability of FC. This study aimed to evaluate the stability of a personalized FC-based network targeting model in individuals with major depressive disorder or schizophrenia with auditory verbal hallucinations. Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, we assessed the model's stability. We employed longer scan durations and cluster methodologies to improve the precision in identifying optimal individual sites. Our findings demonstrate that a scan duration of 28 minutes and the utilization of the cluster method achieved stable identification of individual sites, as evidenced by the intraindividual distance falling below the ~1cm spatial resolution of TMS. The current model provides a feasible approach to obtaining stable personalized TMS targets from the scalp, offering a more accurate method of TMS targeting in clinical applications.
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Affiliation(s)
- Zhengcao Cao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- School of Arts and Communication, Beijing Normal University, Beijing, China
| | - Xiang Xiao
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
| | - Cong Xie
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Lijiang Wei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
| | - Chaozhe Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China
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13
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Lankinen K, Ahveninen J, Jas M, Raij T, Ahlfors SP. Neuronal modeling of magnetoencephalography responses in auditory cortex to auditory and visual stimuli. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.16.545371. [PMID: 37398025 PMCID: PMC10312796 DOI: 10.1101/2023.06.16.545371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Previous studies have demonstrated that auditory cortex activity can be influenced by crosssensory visual inputs. Intracortical recordings in non-human primates (NHP) have suggested a bottom-up feedforward (FF) type laminar profile for auditory evoked but top-down feedback (FB) type for cross-sensory visual evoked activity in the auditory cortex. To test whether this principle applies also to humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimated MEG source waveforms for auditory cortex region of interest, auditory evoked responses showed peaks at 37 and 90 ms and cross-sensory visual responses at 125 ms. The inputs to the auditory cortex were then modeled through FF and FB type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which consists of a neocortical circuit model linking the cellular- and circuit-level mechanisms to MEG. The HNN models suggested that the measured auditory response could be explained by an FF input followed by an FB input, and the crosssensory visual response by an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG/EEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas.
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Affiliation(s)
- Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Mainak Jas
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Seppo P. Ahlfors
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
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14
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Wang X, Leprince Y, Lebenberg J, Langlet C, Mohlberg H, Rivière D, Auzias G, Dickscheid T, Amunts K, Mangin JF. A framework to improve the alignment of individual cytoarchitectonic maps of the Julich-Brain atlas using cortical folding landmarks. Cereb Cortex 2024; 34:bhad538. [PMID: 38236742 DOI: 10.1093/cercor/bhad538] [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: 10/25/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 02/06/2024] Open
Abstract
The segregation of the cortical mantle into cytoarchitectonic areas provides a structural basis for the specialization of different brain regions. In vivo neuroimaging experiments can be linked to this postmortem cytoarchitectonic parcellation via Julich-Brain. This atlas embeds probabilistic maps that account for inter-individual variability in the localization of cytoarchitectonic areas in the reference spaces targeted by spatial normalization. We built a framework to improve the alignment of architectural areas across brains using cortical folding landmarks. This framework, initially designed for in vivo imaging, was adapted to postmortem histological data. We applied this to the first 14 brains used to establish the Julich-Brain atlas to infer a refined atlas with more focal probabilistic maps. The improvement achieved is significant in the primary regions and some of the associative areas. This framework also provides a tool for exploring the relationship between cortical folding patterns and cytoarchitectonic areas in different cortical regions to establish new landmarks in the remainder of the cortex.
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Affiliation(s)
- Xiaoyu Wang
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Saclay, France
| | - Yann Leprince
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Saclay, France
- UNIACT, NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Jessica Lebenberg
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Saclay, France
- Lariboisière University Hospital, APHP, Translational Neurovascular Centre and Department of Neurology, FHU NeuroVasc, Paris, France
| | - Clement Langlet
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Saclay, France
| | - Hartmut Mohlberg
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, D-52425 Jülich, Germany
| | - Denis Rivière
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Saclay, France
| | - Guillaume Auzias
- Institut de Neurosciences de la Timone, UMR 7289, Aix Marseille Université, CNRS, Marseille, France
| | - Timo Dickscheid
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, D-52425 Jülich, Germany
- Institute of Computer Science, Heinrich-Heine University Düsseldorf, D-40225 Düsseldorf, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, D-52425 Jülich, Germany
- Cecile und Oskar Vogt Institut für Hirnforschung, University Hospital Düsseldorf, Heinrich-Heine Universität Düsseldorf, D-40225 Düsseldorf, Germany
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15
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Tucciarelli R, Ejaz N, Wesselink DB, Kolli V, Hodgetts CJ, Diedrichsen J, Makin TR. Does Ipsilateral Remapping Following Hand Loss Impact Motor Control of the Intact Hand? J Neurosci 2024; 44:e0948232023. [PMID: 38050100 PMCID: PMC10860625 DOI: 10.1523/jneurosci.0948-23.2023] [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: 05/23/2023] [Revised: 10/31/2023] [Accepted: 11/21/2023] [Indexed: 12/06/2023] Open
Abstract
What happens once a cortical territory becomes functionally redundant? We studied changes in brain function and behavior for the remaining hand in humans (male and female) with either a missing hand from birth (one-handers) or due to amputation. Previous studies reported that amputees, but not one-handers, show increased ipsilateral activity in the somatosensory territory of the missing hand (i.e., remapping). We used a complex finger task to explore whether this observed remapping in amputees involves recruiting more neural resources to support the intact hand to meet greater motor control demands. Using basic fMRI analysis, we found that only amputees had more ipsilateral activity when motor demand increased; however, this did not match any noticeable improvement in their behavioral task performance. More advanced multivariate fMRI analyses showed that amputees had stronger and more typical representation-relative to controls' contralateral hand representation-compared with one-handers. This suggests that in amputees, both hand areas work together more collaboratively, potentially reflecting the intact hand's efference copy. One-handers struggled to learn difficult finger configurations, but this did not translate to differences in univariate or multivariate activity relative to controls. Additional white matter analysis provided conclusive evidence that the structural connectivity between the two hand areas did not vary across groups. Together, our results suggest that enhanced activity in the missing hand territory may not reflect intact hand function. Instead, we suggest that plasticity is more restricted than generally assumed and may depend on the availability of homologous pathways acquired early in life.
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Affiliation(s)
- Raffaele Tucciarelli
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom
- Institute of Cognitive Neuroscience, University College London, London WC1N 3AZ, United Kingdom
| | - Naveed Ejaz
- Departments of Statistical and Actuarial Sciences and Computer Science, Western University, London, Ontario N6A 5B7, Canada
| | - Daan B Wesselink
- WIN Centre, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford OX3 9DU, United Kingdom
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Vijay Kolli
- Queen Mary's Hospital, London SW15 5PN, United Kingdom
| | - Carl J Hodgetts
- CUBRIC, School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom
- Royal Holloway, University of London, Egham TW20 0EX, United Kingdom
| | - Jörn Diedrichsen
- Departments of Statistical and Actuarial Sciences and Computer Science, Western University, London, Ontario N6A 5B7, Canada
- Brain and Mind Institute, Western University, London, Ontario N6A 3K7, Canada
| | - Tamar R Makin
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom
- Institute of Cognitive Neuroscience, University College London, London WC1N 3AZ, United Kingdom
- WIN Centre, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford OX3 9DU, United Kingdom
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16
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Abaci Turk E, Yun HJ, Feldman HA, Lee JY, Lee HJ, Bibbo C, Zhou C, Tamen R, Grant PE, Im K. Association between placental oxygen transport and fetal brain cortical development: a study in monochorionic diamniotic twins. Cereb Cortex 2024; 34:bhad383. [PMID: 37885155 PMCID: PMC11032198 DOI: 10.1093/cercor/bhad383] [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: 06/28/2023] [Revised: 09/25/2023] [Accepted: 09/26/2023] [Indexed: 10/28/2023] Open
Abstract
Normal cortical growth and the resulting folding patterns are crucial for normal brain function. Although cortical development is largely influenced by genetic factors, environmental factors in fetal life can modify the gene expression associated with brain development. As the placenta plays a vital role in shaping the fetal environment, affecting fetal growth through the exchange of oxygen and nutrients, placental oxygen transport might be one of the environmental factors that also affect early human cortical growth. In this study, we aimed to assess the placental oxygen transport during maternal hyperoxia and its impact on fetal brain development using MRI in identical twins to control for genetic and maternal factors. We enrolled 9 pregnant subjects with monochorionic diamniotic twins (30.03 ± 2.39 gestational weeks [mean ± SD]). We observed that the fetuses with slower placental oxygen delivery had reduced volumetric and surface growth of the cerebral cortex. Moreover, when the difference between placenta oxygen delivery increased between the twin pairs, sulcal folding patterns were more divergent. Thus, there is a significant relationship between placental oxygen transport and fetal brain cortical growth and folding in monochorionic twins.
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Affiliation(s)
- Esra Abaci Turk
- Department of Pediatrics, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
- Division of Newborn Medicine, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115, United States
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, 401 Park Dr, Boston, MA 02115, United States
| | - Hyuk Jin Yun
- Department of Pediatrics, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
- Division of Newborn Medicine, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115, United States
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, 401 Park Dr, Boston, MA 02115, United States
| | - Henry A Feldman
- Department of Pediatrics, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
- Division of Newborn Medicine, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115, United States
- Institutional Centers for Clinical and Translational Research, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115, United States
| | - Joo Young Lee
- Department of Pediatrics, Hanyang University College of Medicine, 222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea
| | - Hyun Ju Lee
- Department of Pediatrics, Hanyang University College of Medicine, 222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea
| | - Carolina Bibbo
- Department of Obstetrics and Gynecology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, United States
| | - Cindy Zhou
- Division of Newborn Medicine, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115, United States
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, 401 Park Dr, Boston, MA 02115, United States
| | - Rubii Tamen
- Division of Newborn Medicine, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115, United States
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, 401 Park Dr, Boston, MA 02115, United States
| | - Patricia Ellen Grant
- Department of Pediatrics, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
- Division of Newborn Medicine, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115, United States
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, 401 Park Dr, Boston, MA 02115, United States
- Department of Radiology, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115, United States
| | - Kiho Im
- Department of Pediatrics, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
- Division of Newborn Medicine, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115, United States
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, 401 Park Dr, Boston, MA 02115, United States
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17
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Da X, Hempel E, Ou Y, Rowe OE, Malchano Z, Hajós M, Kern R, Megerian JT, Cimenser A. Noninvasive Gamma Sensory Stimulation May Reduce White Matter and Myelin Loss in Alzheimer's Disease. J Alzheimers Dis 2024; 97:359-372. [PMID: 38073386 PMCID: PMC10789351 DOI: 10.3233/jad-230506] [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] [Accepted: 10/27/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND Patients with Alzheimer's disease (AD) demonstrate progressive white matter atrophy and myelin loss. Restoring myelin content or preventing demyelination has been suggested as a therapeutic approach for AD. OBJECTIVE Herein, we investigate the effects of non-invasive, combined visual and auditory gamma-sensory stimulation on white matter atrophy and myelin content loss in patients with AD. METHODS In this study, we used the magnetic resonance imaging (MRI) data from the OVERTURE study (NCT03556280), a randomized, controlled, clinical trial in which active treatment participants received daily, non-invasive, combined visual and auditory, 40 Hz stimulation for six months. A subset of OVERTURE participants who meet the inclusion criteria for detailed white matter (N = 38) and myelin content (N = 36) assessments are included in the analysis. White matter volume assessments were performed using T1-weighted MRI, and myelin content assessments were performed using T1-weighted/T2-weighted MRI. Treatment effects on white matter atrophy and myelin content loss were assessed. RESULTS Combined visual and auditory gamma-sensory stimulation treatment is associated with reduced total and regional white matter atrophy and myelin content loss in active treatment participants compared to sham treatment participants. Across white matter structures evaluated, the most significant changes were observed in the entorhinal region. CONCLUSIONS The study results suggest that combined visual and auditory gamma-sensory stimulation may modulate neuronal network function in AD in part by reducing white matter atrophy and myelin content loss. Furthermore, the entorhinal region MRI outcomes may have significant implications for early disease intervention, considering the crucial afferent connections to the hippocampus and entorhinal cortex.
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Affiliation(s)
- Xiao Da
- Cognito Therapeutics, Inc., Cambridge, MA, USA
| | - Evan Hempel
- Cognito Therapeutics, Inc., Cambridge, MA, USA
| | - Yangming Ou
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | | | | | - Mihály Hajós
- Cognito Therapeutics, Inc., Cambridge, MA, USA
- Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Ralph Kern
- Cognito Therapeutics, Inc., Cambridge, MA, USA
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18
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Snyder WE, Vértes PE, Kyriakopoulou V, Wagstyl K, Williams LZJ, Moraczewski D, Thomas AG, Karolis VR, Seidlitz J, Rivière D, Robinson EC, Mangin JF, Raznahan A, Bullmore ET. A bipolar taxonomy of adult human brain sulcal morphology related to timing of fetal sulcation and trans-sulcal gene expression gradients. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.19.572454. [PMID: 38168226 PMCID: PMC10760196 DOI: 10.1101/2023.12.19.572454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
We developed a computational pipeline (now provided as a resource) for measuring morphological similarity between cortical surface sulci to construct a sulcal phenotype network (SPN) from each magnetic resonance imaging (MRI) scan in an adult cohort (N=34,725; 45-82 years). Networks estimated from pairwise similarities of 40 sulci on 5 morphological metrics comprised two clusters of sulci, represented also by the bipolar distribution of sulci on a linear-to-complex dimension. Linear sulci were more heritable and typically located in unimodal cortex; complex sulci were less heritable and typically located in heteromodal cortex. Aligning these results with an independent fetal brain MRI cohort (N=228; 21-36 gestational weeks), we found that linear sulci formed earlier, and the earliest and latest-forming sulci had the least between-adult variation. Using high-resolution maps of cortical gene expression, we found that linear sulcation is mechanistically underpinned by trans-sulcal gene expression gradients enriched for developmental processes.
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Affiliation(s)
- William E Snyder
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, Bethesda, MD, USA
| | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Logan Z J Williams
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Dustin Moraczewski
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Adam G Thomas
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Vyacheslav R Karolis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Jakob Seidlitz
- Lifespan Brain Institute, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Denis Rivière
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Gif-sur-Yvette, 91191, France
| | - Emma C Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Jean-Francois Mangin
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Gif-sur-Yvette, 91191, France
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, Bethesda, MD, USA
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, UK
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19
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Schone HR, Maimon Mor RO, Kollamkulam M, Gerrand C, Woollard A, Kang NV, Baker CI, Makin TR. Stable Cortical Body Maps Before and After Arm Amputation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.13.571314. [PMID: 38168448 PMCID: PMC10760201 DOI: 10.1101/2023.12.13.571314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Neuroscientists have long debated the adult brain's capacity to reorganize itself in response to injury. A driving model for studying plasticity has been limb amputation. For decades, it was believed that amputation triggers large-scale reorganization of cortical body resources. However, these studies have relied on cross-sectional observations post-amputation, without directly tracking neural changes. Here, we longitudinally followed adult patients with planned arm amputations and measured hand and face representations, before and after amputation. By interrogating the representational structure elicited from movements of the hand (pre-amputation) and phantom hand (post-amputation), we demonstrate that hand representation is unaltered. Further, we observed no evidence for lower face (lip) reorganization into the deprived hand region. Collectively, our findings provide direct and decisive evidence that amputation does not trigger large-scale cortical reorganization.
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Affiliation(s)
- Hunter R. Schone
- Institute of Cognitive Neuroscience, University College London, London, UK
- Laboratory of Brain & Cognition, National Institutes of Mental Health, National Institutes of Health, Bethesda, Maryland, USA
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
| | - Roni O. Maimon Mor
- Institute of Cognitive Neuroscience, University College London, London, UK
- Department of Experimental Psychology, University College London, London, UK
- UCL Institute of Ophthalmology, University College London, London, UK
| | - Mathew Kollamkulam
- Institute of Cognitive Neuroscience, University College London, London, UK
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Craig Gerrand
- Department of Orthopaedic Oncology, Royal National Orthopaedic Hospital NHS Trust, Stanmore, Middlesex, UK
| | | | - Norbert V. Kang
- Plastic Surgery Department, Royal Free Hospital NHS Trust, London, UK
| | - Chris I. Baker
- Laboratory of Brain & Cognition, National Institutes of Mental Health, National Institutes of Health, Bethesda, Maryland, USA
| | - Tamar R. Makin
- Institute of Cognitive Neuroscience, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, London, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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20
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DeKraker J, Palomero-Gallagher N, Kedo O, Ladbon-Bernasconi N, Muenzing SEA, Axer M, Amunts K, Khan AR, Bernhardt BC, Evans AC. Evaluation of surface-based hippocampal registration using ground-truth subfield definitions. eLife 2023; 12:RP88404. [PMID: 37956092 PMCID: PMC10642966 DOI: 10.7554/elife.88404] [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] [Indexed: 11/15/2023] Open
Abstract
The hippocampus is an archicortical structure, consisting of subfields with unique circuits. Understanding its microstructure, as proxied by these subfields, can improve our mechanistic understanding of learning and memory and has clinical potential for several neurological disorders. One prominent issue is how to parcellate, register, or retrieve homologous points between two hippocampi with grossly different morphologies. Here, we present a surface-based registration method that solves this issue in a contrast-agnostic, topology-preserving manner. Specifically, the entire hippocampus is first analytically unfolded, and then samples are registered in 2D unfolded space based on thickness, curvature, and gyrification. We demonstrate this method in seven 3D histology samples and show superior alignment with respect to subfields using this method over more conventional registration approaches.
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Affiliation(s)
- Jordan DeKraker
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
- C. & O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine-UniversityDüsseldorfGermany
| | - Olga Kedo
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | | | - Sascha EA Muenzing
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - Markus Axer
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
- C. & O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine-UniversityDüsseldorfGermany
| | - Ali R Khan
- Robarts Research Institute, University of Western OntarioLondonCanada
| | - Boris C Bernhardt
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Alan C Evans
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
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21
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Denis C, Dabbs K, Nair VA, Mathis J, Almane DN, Lakshmanan A, Nencka A, Birn RM, Conant L, Humphries C, Felton E, Raghavan M, DeYoe EA, Binder JR, Hermann B, Prabhakaran V, Bendlin BB, Meyerand ME, Boly M, Struck AF. T1-/T2-weighted ratio reveals no alterations to gray matter myelination in temporal lobe epilepsy. Ann Clin Transl Neurol 2023; 10:2149-2154. [PMID: 37872734 PMCID: PMC10647008 DOI: 10.1002/acn3.51653] [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: 02/04/2022] [Revised: 05/29/2022] [Accepted: 06/09/2022] [Indexed: 10/25/2023] Open
Abstract
Short-range functional connectivity in the limbic network is increased in patients with temporal lobe epilepsy (TLE), and recent studies have shown that cortical myelin content correlates with fMRI connectivity. We thus hypothesized that myelin may increase progressively in the epileptic network. We compared T1w/T2w gray matter myelin maps between TLE patients and age-matched controls and assessed relationships between myelin and aging. While both TLE patients and healthy controls exhibited increased T1w/T2w intensity with age, we found no evidence for significant group-level aberrations in overall myelin content or myelin changes through time in TLE.
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Affiliation(s)
- Colin Denis
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Kevin Dabbs
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Veena A. Nair
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Jedidiah Mathis
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Dace N. Almane
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | | | - Andrew Nencka
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Rasmus M. Birn
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of PsychiatryUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Lisa Conant
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Colin Humphries
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Elizabeth Felton
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Manoj Raghavan
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Edgar A. DeYoe
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Jeffrey R. Binder
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Bruce Hermann
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Vivek Prabhakaran
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Barbara B. Bendlin
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Mary E. Meyerand
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of Biomedical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Mélanie Boly
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of PsychiatryUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Aaron F. Struck
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- William S. Middleton Veterans Administration HospitalMadisonWisconsinUSA
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22
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Li J, Tuckute G, Fedorenko E, Edlow BL, Fischl B, Dalca AV. Joint cortical registration of geometry and function using semi-supervised learning. ARXIV 2023:arXiv:2303.01592v4. [PMID: 37744470 PMCID: PMC10516111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces. Existing iterative and learning-based approaches focus on accurate registration of folding patterns of the cerebral cortex, and assume that geometry predicts function and thus functional areas will also be well aligned. However, structure/functional variability of anatomically corresponding areas across subjects has been widely reported. In this work, we introduce a learning-based cortical registration framework, JOSA, which jointly aligns folding patterns and functional maps while simultaneously learning an optimal atlas. We demonstrate that JOSA can substantially improve registration performance in both anatomical and functional domains over existing methods. By employing a semi-supervised training strategy, the proposed framework obviates the need for functional data during inference, enabling its use in broad neuroscientific domains where functional data may not be observed. The source code of JOSA will be released to the public at https://voxelmorph.net.
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Affiliation(s)
- Jian Li
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School
| | - Greta Tuckute
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
- McGovern Institute for Brain Research, Massachusetts Institute of Technology
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
- McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Program in Speech Hearing Bioscience and Technology, Harvard University
| | - Brian L Edlow
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School
| | - Bruce Fischl
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
- Harvard-MIT Program in Health Sciences and Technology
| | - Adrian V Dalca
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
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23
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Guo Y, Chen Q, Choi GPT, Lui LM. Automatic landmark detection and registration of brain cortical surfaces via quasi-conformal geometry and convolutional neural networks. Comput Biol Med 2023; 163:107185. [PMID: 37418897 DOI: 10.1016/j.compbiomed.2023.107185] [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: 12/05/2022] [Revised: 05/24/2023] [Accepted: 06/13/2023] [Indexed: 07/09/2023]
Abstract
In medical imaging, surface registration is extensively used for performing systematic comparisons between anatomical structures, with a prime example being the highly convoluted brain cortical surfaces. To obtain a meaningful registration, a common approach is to identify prominent features on the surfaces and establish a low-distortion mapping between them with the feature correspondence encoded as landmark constraints. Prior registration works have primarily focused on using manually labeled landmarks and solving highly nonlinear optimization problems, which are time-consuming and hence hinder practical applications. In this work, we propose a novel framework for the automatic landmark detection and registration of brain cortical surfaces using quasi-conformal geometry and convolutional neural networks. We first develop a landmark detection network (LD-Net) that allows for the automatic extraction of landmark curves given two prescribed starting and ending points based on the surface geometry. We then utilize the detected landmarks and quasi-conformal theory for achieving the surface registration. Specifically, we develop a coefficient prediction network (CP-Net) for predicting the Beltrami coefficients associated with the desired landmark-based registration and a mapping network called the disk Beltrami solver network (DBS-Net) for generating quasi-conformal mappings from the predicted Beltrami coefficients, with the bijectivity guaranteed by quasi-conformal theory. Experimental results are presented to demonstrate the effectiveness of our proposed framework. Altogether, our work paves a new way for surface-based morphometry and medical shape analysis.
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Affiliation(s)
- Yuchen Guo
- Department of Mathematics, The Chinese University of Hong Kong, Hong Kong
| | - Qiguang Chen
- Department of Mathematics, The Chinese University of Hong Kong, Hong Kong
| | - Gary P T Choi
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lok Ming Lui
- Department of Mathematics, The Chinese University of Hong Kong, Hong Kong.
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24
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Gallardo G, Eichner C, Sherwood CC, Hopkins WD, Anwander A, Friederici AD. Morphological evolution of language-relevant brain areas. PLoS Biol 2023; 21:e3002266. [PMID: 37656748 PMCID: PMC10501646 DOI: 10.1371/journal.pbio.3002266] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 09/14/2023] [Accepted: 07/21/2023] [Indexed: 09/03/2023] Open
Abstract
Human language is supported by a cortical network involving Broca's area, which comprises Brodmann Areas 44 and 45 (BA44 and BA45). While cytoarchitectonic homolog areas have been identified in nonhuman primates, it remains unknown how these regions evolved to support human language. Here, we use histological data and advanced cortical registration methods to precisely compare the morphology of BA44 and BA45 in humans and chimpanzees. We found a general expansion of Broca's areas in humans, with the left BA44 enlarging the most, growing anteriorly into a region known to process syntax. Together with recent functional and receptorarchitectural studies, our findings support the conclusion that BA44 evolved from an action-related region to a bipartite system, with a posterior portion supporting action and an anterior portion supporting syntactic processes. Our findings add novel insights to the longstanding debate on the relationship between language and action, and the evolution of Broca's area.
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Affiliation(s)
- Guillermo Gallardo
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Cornelius Eichner
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Chet C. Sherwood
- Department of Anthropology, The George Washington University, Washington, DC, United States of America
| | - William D. Hopkins
- Department of Comparative Medicine, Michale E. Keeling Center for Comparative Medicine, The University of Texas MD Anderson Cancer Center, Bastrop, Texas, United States of America
| | - Alfred Anwander
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Angela D. Friederici
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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25
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Du J, DiNicola LM, Angeli PA, Saadon-Grosman N, Sun W, Kaiser S, Ladopoulou J, Xue A, Yeo BTT, Eldaief MC, Buckner RL. Within-Individual Organization of the Human Cerebral Cortex: Networks, Global Topography, and Function. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.08.552437. [PMID: 37609246 PMCID: PMC10441314 DOI: 10.1101/2023.08.08.552437] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
The human cerebral cortex is populated by specialized regions that are organized into networks. Here we estimated networks using a Multi-Session Hierarchical Bayesian Model (MS-HBM) applied to intensively sampled within-individual functional MRI (fMRI) data. The network estimation procedure was initially developed and tested in two participants (each scanned 31 times) and then prospectively applied to 15 new participants (each scanned 8 to 11 times). Detailed analysis of the networks revealed a global organization. Locally organized first-order sensory and motor networks were surrounded by spatially adjacent second-order networks that also linked to distant regions. Third-order networks each possessed regions distributed widely throughout association cortex. Moreover, regions of distinct third-order networks displayed side-by-side juxtapositions with a pattern that repeated similarly across multiple cortical zones. We refer to these as Supra-Areal Association Megaclusters (SAAMs). Within each SAAM, two candidate control regions were typically adjacent to three separate domain-specialized regions. Independent task data were analyzed to explore functional response properties. The somatomotor and visual first-order networks responded to body movements and visual stimulation, respectively. A subset of the second-order networks responded to transients in an oddball detection task, consistent with a role in orienting to salient or novel events. The third-order networks, including distinct regions within each SAAM, showed two levels of functional specialization. Regions linked to candidate control networks responded to working memory load across multiple stimulus domains. The remaining regions within each SAAM did not track working memory load but rather dissociated across language, social, and spatial / episodic processing domains. These results support a model of the cerebral cortex in which progressively higher-order networks nest outwards from primary sensory and motor cortices. Within the apex zones of association cortex there is specialization of large-scale networks that divides domain-flexible from domain-specialized regions repeatedly across parietal, temporal, and prefrontal cortices. We discuss implications of these findings including how repeating organizational motifs may emerge during development.
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Affiliation(s)
- Jingnan Du
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Lauren M DiNicola
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Peter A Angeli
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Noam Saadon-Grosman
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Wendy Sun
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Stephanie Kaiser
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Joanna Ladopoulou
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Aihuiping Xue
- Centre for Sleep & Cognition & Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Mark C Eldaief
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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26
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Chavoshnejad P, Chen L, Yu X, Hou J, Filla N, Zhu D, Liu T, Li G, Razavi MJ, Wang X. An integrated finite element method and machine learning algorithm for brain morphology prediction. Cereb Cortex 2023; 33:9354-9366. [PMID: 37288479 PMCID: PMC10393506 DOI: 10.1093/cercor/bhad208] [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: 03/30/2023] [Revised: 05/18/2023] [Accepted: 05/19/2023] [Indexed: 06/09/2023] Open
Abstract
The human brain development experiences a complex evolving cortical folding from a smooth surface to a convoluted ensemble of folds. Computational modeling of brain development has played an essential role in better understanding the process of cortical folding, but still leaves many questions to be answered. A major challenge faced by computational models is how to create massive brain developmental simulations with affordable computational sources to complement neuroimaging data and provide reliable predictions for brain folding. In this study, we leveraged the power of machine learning in data augmentation and prediction to develop a machine-learning-based finite element surrogate model to expedite brain computational simulations, predict brain folding morphology, and explore the underlying folding mechanism. To do so, massive finite element method (FEM) mechanical models were run to simulate brain development using the predefined brain patch growth models with adjustable surface curvature. Then, a GAN-based machine learning model was trained and validated with these produced computational data to predict brain folding morphology given a predefined initial configuration. The results indicate that the machine learning models can predict the complex morphology of folding patterns, including 3-hinge gyral folds. The close agreement between the folding patterns observed in FEM results and those predicted by machine learning models validate the feasibility of the proposed approach, offering a promising avenue to predict the brain development with given fetal brain configurations.
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Affiliation(s)
- Poorya Chavoshnejad
- Department of Mechanical Engineering, Binghamton University, Binghamton, NY 13902, United States
| | - Liangjun Chen
- Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Xiaowei Yu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, United States
| | - Jixin Hou
- School of ECAM, University of Georgia, Athens, GA 30602, United States
| | - Nicholas Filla
- School of ECAM, University of Georgia, Athens, GA 30602, United States
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, United States
| | - Tianming Liu
- School of Computing, University of Georgia, Athens, GA 30602, United States
| | - Gang Li
- Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Mir Jalil Razavi
- Department of Mechanical Engineering, Binghamton University, Binghamton, NY 13902, United States
| | - Xianqiao Wang
- School of ECAM, University of Georgia, Athens, GA 30602, United States
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27
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Broday-Dvir R, Norman Y, Harel M, Mehta AD, Malach R. Perceptual stability reflected in neuronal pattern similarities in human visual cortex. Cell Rep 2023; 42:112614. [PMID: 37285270 DOI: 10.1016/j.celrep.2023.112614] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/14/2023] [Accepted: 05/22/2023] [Indexed: 06/09/2023] Open
Abstract
The magnitude of neuronal activation is commonly considered a critical factor for conscious perception of visual content. However, this dogma contrasts with the phenomenon of rapid adaptation, in which the magnitude of neuronal activation drops dramatically in a rapid manner while the visual stimulus and the conscious experience it elicits remain stable. Here, we report that the profiles of multi-site activation patterns and their relational geometry-i.e., the similarity distances between activation patterns, as revealed using intracranial electroencephalographic (iEEG) recordings-are sustained during extended visual stimulation despite the major magnitude decrease. These results are compatible with the hypothesis that conscious perceptual content is associated with the neuronal pattern profiles and their similarity distances, rather than the overall activation magnitude, in human visual cortex.
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Affiliation(s)
- Rotem Broday-Dvir
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Yitzhak Norman
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Michal Harel
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Ashesh D Mehta
- Department of Neurosurgery, Zucker School of Medicine at Hofstra/Northwell, and the Feinstein Institute for Medical Research, Manhasset, NY 11030, USA
| | - Rafael Malach
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.
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28
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de Sousa AA, Beaudet A, Calvey T, Bardo A, Benoit J, Charvet CJ, Dehay C, Gómez-Robles A, Gunz P, Heuer K, van den Heuvel MP, Hurst S, Lauters P, Reed D, Salagnon M, Sherwood CC, Ströckens F, Tawane M, Todorov OS, Toro R, Wei Y. From fossils to mind. Commun Biol 2023; 6:636. [PMID: 37311857 PMCID: PMC10262152 DOI: 10.1038/s42003-023-04803-4] [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: 12/17/2022] [Accepted: 04/04/2023] [Indexed: 06/15/2023] Open
Abstract
Fossil endocasts record features of brains from the past: size, shape, vasculature, and gyrification. These data, alongside experimental and comparative evidence, are needed to resolve questions about brain energetics, cognitive specializations, and developmental plasticity. Through the application of interdisciplinary techniques to the fossil record, paleoneurology has been leading major innovations. Neuroimaging is shedding light on fossil brain organization and behaviors. Inferences about the development and physiology of the brains of extinct species can be experimentally investigated through brain organoids and transgenic models based on ancient DNA. Phylogenetic comparative methods integrate data across species and associate genotypes to phenotypes, and brains to behaviors. Meanwhile, fossil and archeological discoveries continuously contribute new knowledge. Through cooperation, the scientific community can accelerate knowledge acquisition. Sharing digitized museum collections improves the availability of rare fossils and artifacts. Comparative neuroanatomical data are available through online databases, along with tools for their measurement and analysis. In the context of these advances, the paleoneurological record provides ample opportunity for future research. Biomedical and ecological sciences can benefit from paleoneurology's approach to understanding the mind as well as its novel research pipelines that establish connections between neuroanatomy, genes and behavior.
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Affiliation(s)
| | - Amélie Beaudet
- Laboratoire de Paléontologie, Évolution, Paléoécosystèmes et Paléoprimatologie (PALEVOPRIM), UMR 7262 CNRS & Université de Poitiers, Poitiers, France.
- University of Cambridge, Cambridge, UK.
| | - Tanya Calvey
- Division of Clinical Anatomy and Biological Anthropology, University of Cape Town, Cape Town, South Africa.
| | - Ameline Bardo
- UMR 7194, CNRS-MNHN, Département Homme et Environnement, Musée de l'Homme, Paris, France
- Skeletal Biology Research Centre, School of Anthropology and Conservation, University of Kent, Canterbury, UK
| | - Julien Benoit
- Evolutionary Studies Institute, University of the Witwatersrand, Johannesburg, South Africa
| | - Christine J Charvet
- Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, AL, USA
| | - Colette Dehay
- University of Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, F-69500, Bron, France
| | | | - Philipp Gunz
- Department of Human Origins, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, D-04103, Leipzig, Germany
| | - Katja Heuer
- Institut Pasteur, Université Paris Cité, Unité de Neuroanatomie Appliquée et Théorique, F-75015, Paris, France
| | | | - Shawn Hurst
- University of Indianapolis, Indianapolis, IN, USA
| | - Pascaline Lauters
- Institut royal des Sciences naturelles, Direction Opérationnelle Terre et Histoire de la Vie, Brussels, Belgium
| | - Denné Reed
- Department of Anthropology, University of Texas at Austin, Austin, TX, USA
| | - Mathilde Salagnon
- CNRS, CEA, IMN, GIN, UMR 5293, Université Bordeaux, Bordeaux, France
- PACEA UMR 5199, CNRS, Université Bordeaux, Pessac, France
| | - Chet C Sherwood
- Department of Anthropology, The George Washington University, Washington, DC, USA
| | - Felix Ströckens
- C. & O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Mirriam Tawane
- Ditsong National Museum of Natural History, Pretoria, South Africa
| | - Orlin S Todorov
- School of Natural Sciences, Macquarie University, Sydney, NSW, Australia
| | - Roberto Toro
- Institut Pasteur, Université Paris Cité, Unité de Neuroanatomie Appliquée et Théorique, F-75015, Paris, France
| | - Yongbin Wei
- Beijing University of Posts and Telecommunications, Beijing, China
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29
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Chen X, Affourtit J, Ryskin R, Regev TI, Norman-Haignere S, Jouravlev O, Malik-Moraleda S, Kean H, Varley R, Fedorenko E. The human language system, including its inferior frontal component in "Broca's area," does not support music perception. Cereb Cortex 2023; 33:7904-7929. [PMID: 37005063 PMCID: PMC10505454 DOI: 10.1093/cercor/bhad087] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 04/04/2023] Open
Abstract
Language and music are two human-unique capacities whose relationship remains debated. Some have argued for overlap in processing mechanisms, especially for structure processing. Such claims often concern the inferior frontal component of the language system located within "Broca's area." However, others have failed to find overlap. Using a robust individual-subject fMRI approach, we examined the responses of language brain regions to music stimuli, and probed the musical abilities of individuals with severe aphasia. Across 4 experiments, we obtained a clear answer: music perception does not engage the language system, and judgments about music structure are possible even in the presence of severe damage to the language network. In particular, the language regions' responses to music are generally low, often below the fixation baseline, and never exceed responses elicited by nonmusic auditory conditions, like animal sounds. Furthermore, the language regions are not sensitive to music structure: they show low responses to both intact and structure-scrambled music, and to melodies with vs. without structural violations. Finally, in line with past patient investigations, individuals with aphasia, who cannot judge sentence grammaticality, perform well on melody well-formedness judgments. Thus, the mechanisms that process structure in language do not appear to process music, including music syntax.
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Affiliation(s)
- Xuanyi Chen
- Department of Cognitive Sciences, Rice University, TX 77005, United States
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Josef Affourtit
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Rachel Ryskin
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Department of Cognitive & Information Sciences, University of California, Merced, Merced, CA 95343, United States
| | - Tamar I Regev
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Samuel Norman-Haignere
- Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, United States
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, United States
| | - Olessia Jouravlev
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Department of Cognitive Science, Carleton University, Ottawa, ON, Canada
| | - Saima Malik-Moraleda
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- The Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA 02138, United States
| | - Hope Kean
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Rosemary Varley
- Psychology & Language Sciences, UCL, London, WCN1 1PF, United Kingdom
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- The Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA 02138, United States
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30
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Yan X, Kong R, Xue A, Yang Q, Orban C, An L, Holmes AJ, Qian X, Chen J, Zuo XN, Zhou JH, Fortier MV, Tan AP, Gluckman P, Chong YS, Meaney MJ, Bzdok D, Eickhoff SB, Yeo BTT. Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity. Neuroimage 2023; 273:120010. [PMID: 36918136 PMCID: PMC10212507 DOI: 10.1016/j.neuroimage.2023.120010] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/25/2023] [Accepted: 03/08/2023] [Indexed: 03/13/2023] Open
Abstract
Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local-global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Yan2023_homotopic).
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Affiliation(s)
- Xiaoxuan Yan
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Ru Kong
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Aihuiping Xue
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Qing Yang
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Csaba Orban
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Lijun An
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Avram J Holmes
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, Unites States of America
| | - Xing Qian
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Jianzhong Chen
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning/IDG McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; National Basic Public Science Data Center, China
| | - Juan Helen Zhou
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Marielle V Fortier
- Department of Diagnostic and Interventional Imaging, KK Women's and Children's Hospital, Singapore; Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore
| | - Ai Peng Tan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore; Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Peter Gluckman
- UK Centre for Human Evolution, Adaptation and Disease, Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Yap Seng Chong
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore; Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Michael J Meaney
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Danilo Bzdok
- Department of Biomedical Engineering, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Mila - Quebec AI Institute, Montreal, QC, Canada
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, Unites States of America.
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31
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Surgent O, Guerrero-Gonzalez J, Dean DC, Kirk GR, Adluru N, Kecskemeti SR, Alexander AL, Travers BG. How we get a grip: Microstructural neural correlates of manual grip strength in children. Neuroimage 2023; 273:120117. [PMID: 37062373 PMCID: PMC10161685 DOI: 10.1016/j.neuroimage.2023.120117] [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: 12/09/2022] [Revised: 03/23/2023] [Accepted: 04/13/2023] [Indexed: 04/18/2023] Open
Abstract
Maximal grip strength is associated with a variety of health-related outcome measures and thus may be reflective of the efficiency of foundational brain-body communication. Non-human primate models of grip strength strongly implicate the cortical lateral grasping network, but little is known about the translatability of these models to human children. Further, it is unclear how supplementary networks that provide proprioceptive information and cerebellar-based motor command modification are associated with maximal grip strength. Therefore, this study employed high resolution, multi-shell diffusion and quantitative T1 imaging to examine how variations in lateral grasping, proprioception input, and cortico-cerebellar modification network white matter microstructure are associated with variations in grip strength across 70 children. Results indicated that stronger grip strength was associated with higher lateral grasping and proprioception input network fractional anisotropy and R1, indirect measures consistent with stronger microstructural coherence and increased myelination. No relationships were found in the cerebellar modification network. These results provide a neurobiological mechanism of grip behavior in children which suggests that increased myelination of cortical sensory and motor pathways is associated with stronger grip. This neurobiological mechanism may be a signature of pediatric neuro-motor behavior more broadly as evidenced by the previously demonstrated relationships between grip strength and behavioral outcome measures across a variety of clinical and non-clinical populations.
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Affiliation(s)
- Olivia Surgent
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States; Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, United States
| | - Jose Guerrero-Gonzalez
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States
| | - Douglas C Dean
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States; Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, United States
| | - Gregory R Kirk
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States; Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | | | - Andrew L Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Brittany G Travers
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States; Occupational Therapy Program in the Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States.
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32
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Zhang S, Zhang T, He Z, Li X, Zhang L, Zhu D, Jiang X, Liu T, Han J, Guo L. Gyral peaks and patterns in human brains. Cereb Cortex 2023; 33:6708-6722. [PMID: 36646465 PMCID: PMC10422926 DOI: 10.1093/cercor/bhac537] [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: 10/08/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 01/18/2023] Open
Abstract
Cortical folding patterns are related to brain function, cognition, and behavior. Since the relationship has not been fully explained on a coarse scale, many efforts have been devoted to the identification of finer grained cortical landmarks, such as sulcal pits and gyral peaks, which were found to remain invariant across subjects and ages and the invariance may be related to gene mediated proto-map. However, gyral peaks were only investigated on macaque monkey brains, but not on human brains where the investigation is challenged due to high inter-individual variabilities. To this end, in this work, we successfully identified 96 gyral peaks both on the left and right hemispheres of human brains, respectively. These peaks are spatially consistent across individuals. Higher or sharper peaks are more consistent across subjects. Both structural and functional graph metrics of peaks are significantly different from other cortical regions, and more importantly, these nodal graph metrics are anti-correlated with the spatial consistency metrics within peaks. In addition, the distribution of peaks and various cortical anatomical, structural/functional connective features show hemispheric symmetry. These findings provide new clues to understanding the cortical landmarks, as well as their relationship with brain functions, cognition, behavior in both healthy and aberrant brains.
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Affiliation(s)
- Songyao Zhang
- School of Automation, School of Information Technology, and School of Life Science and Technology, Northwestern Polytechnical University, Xi’an 710000, China
| | - Tuo Zhang
- School of Automation, School of Information Technology, and School of Life Science and Technology, Northwestern Polytechnical University, Xi’an 710000, China
| | - Zhibin He
- School of Automation, School of Information Technology, and School of Life Science and Technology, Northwestern Polytechnical University, Xi’an 710000, China
| | - Xiao Li
- School of Automation, School of Information Technology, and School of Life Science and Technology, Northwest University, Xi’an, China
| | - Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States
| | - Xi Jiang
- School of Automation, School of Information Technology, and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30605, United States
| | - Junwei Han
- School of Automation, School of Information Technology, and School of Life Science and Technology, Northwestern Polytechnical University, Xi’an 710000, China
| | - Lei Guo
- School of Automation, School of Information Technology, and School of Life Science and Technology, Northwestern Polytechnical University, Xi’an 710000, China
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33
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Amiez C, Sallet J, Giacometti C, Verstraete C, Gandaux C, Morel-Latour V, Meguerditchian A, Hadj-Bouziane F, Ben Hamed S, Hopkins WD, Procyk E, Wilson CRE, Petrides M. A revised perspective on the evolution of the lateral frontal cortex in primates. SCIENCE ADVANCES 2023; 9:eadf9445. [PMID: 37205762 DOI: 10.1126/sciadv.adf9445] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 04/14/2023] [Indexed: 05/21/2023]
Abstract
Detailed neuroscientific data from macaque monkeys have been essential in advancing understanding of human frontal cortex function, particularly for regions of frontal cortex without homologs in other model species. However, precise transfer of this knowledge for direct use in human applications requires an understanding of monkey to hominid homologies, particularly whether and how sulci and cytoarchitectonic regions in the frontal cortex of macaques relate to those in hominids. We combine sulcal pattern analysis with resting-state functional magnetic resonance imaging and cytoarchitectonic analysis to show that old-world monkey brains have the same principles of organization as hominid brains, with the notable exception of sulci in the frontopolar cortex. This essential comparative framework provides insights into primate brain evolution and a key tool to drive translation from invasive research in monkeys to human applications.
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Affiliation(s)
- Céline Amiez
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Jérôme Sallet
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
- Wellcome Integrative Neuroimaging Centre, Department of Experimental Psychology, University of Oxford, Oxford OX1 3SR, UK
| | - Camille Giacometti
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Charles Verstraete
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Clémence Gandaux
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Valentine Morel-Latour
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Adrien Meguerditchian
- Laboratoire de Psychologie Cognitive, UMR7290, Université Aix-Marseille, CNRS, 13331 Marseille, France
- Station de Primatologie CNRS, UPS846, 13790 Rousset, France
- Brain and Language Research Institute, Université Aix-Marseille, CNRS, 13604 Aix-en-Provence, France
| | - Fadila Hadj-Bouziane
- Integrative Multisensory Perception Action and Cognition Team (ImpAct), INSERM U1028, CNRS UMR5292, Lyon Neuroscience Research Center (CRNL), Lyon, France; University of Lyon 1, Lyon, France
| | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, UMR5229, CNRS-Université Claude Bernard Lyon I, Bron, France
| | - William D Hopkins
- Department of Comparative Medicine, University of Texas MD Anderson Cancer Center, Bastrop, TX, 78602, USA
| | - Emmanuel Procyk
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Charles R E Wilson
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Michael Petrides
- Department of Neurology and Neurosurgery and Department of Psychology, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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34
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Takemura H, Liu W, Kuribayashi H, Miyata T, Kida I. Evaluation of simultaneous multi-slice readout-segmented diffusion-weighted MRI acquisition in human optic nerve measurements. Magn Reson Imaging 2023; 102:103-114. [PMID: 37149064 DOI: 10.1016/j.mri.2023.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/02/2023] [Accepted: 05/02/2023] [Indexed: 05/08/2023]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is the only available method to measure the tissue properties of white matter tracts in living human brains and has opened avenues for neuroscientific and clinical studies on human white matter. However, dMRI using conventional simultaneous multi-slice (SMS) single-shot echo planar imaging (ssEPI) still presents challenges in the analyses of some specific white matter tracts, such as the optic nerve, which are heavily affected by susceptibility-induced artifacts. In this study, we evaluated dMRI data acquired by using SMS readout-segmented EPI (rsEPI), which aims to reduce susceptibility-induced artifacts by dividing the acquisition space into multiple segments along the readout direction to reduce echo spacing. To this end, we acquired dMRI data from 11 healthy volunteers by using SMS ssEPI and SMS rsEPI, and then compared the dMRI data of the human optic nerve between the SMS ssEPI and SMS rsEPI datasets by visual inspection of the datasets and statistical comparisons of fractional anisotropy (FA) values. In comparison with the SMS ssEPI data, the SMS rsEPI data showed smaller susceptibility-induced distortion and exhibited a significantly higher FA along the optic nerve. In summary, this study demonstrates that despite its prolonged acquisition time, SMS rsEPI is a promising approach for measuring the tissue properties of the optic nerve in living humans and will be useful for future neuroscientific and clinical investigations of this pathway.
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Affiliation(s)
- Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Japan; Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Japan; Graduate Institute for Advanced Studies, SOKENDAI, Hayama, Japan.
| | - Wei Liu
- Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | | | - Toshikazu Miyata
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Japan; Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Japan; Graduate School of Frontier Biosciences, Osaka University, Suita, Japan
| | - Ikuhiro Kida
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Japan; Graduate School of Frontier Biosciences, Osaka University, Suita, Japan
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35
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Hendrickson TJ, Reiners P, Moore LA, Perrone AJ, Alexopoulos D, Lee EG, Styner M, Kardan O, Chamberlain TA, Mummaneni A, Caldas HA, Bower B, Stoyell S, Martin T, Sung S, Fair E, Uriarte-Lopez J, Rueter AR, Yacoub E, Rosenberg MD, Smyser CD, Elison JT, Graham A, Fair DA, Feczko E. BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.22.533696. [PMID: 36993540 PMCID: PMC10055337 DOI: 10.1101/2023.03.22.533696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Objectives Brain segmentation of infant magnetic resonance (MR) images is vitally important in studying developmental mental health and disease. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here, we introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source, community-driven model that relies on data augmentation and a large sample size of manually annotated images to facilitate the production of robust and generalizable brain segmentations. Experimental Design Included in model training and testing were MR brain images on 84 participants with an age range of 0-8 months (median postmenstrual ages of 13.57 months). Using manually annotated real and synthetic segmentation images, the model was trained using a 10-fold cross-validation procedure. Testing occurred on MRI data processed with the DCAN labs infant-ABCD-BIDS processing pipeline using segmentations produced from gold standard manual annotation, joint-label fusion (JLF), and BIBSNet to assess model performance. Principal Observations Using group analyses, results suggest that cortical metrics produced using BIBSNet segmentations outperforms JLF segmentations. Additionally, when analyzing individual differences, BIBSNet segmentations perform even better. Conclusions BIBSNet segmentation shows marked improvement over JLF segmentations across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF and can be easily included in other processing pipelines.
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Affiliation(s)
- Timothy J Hendrickson
- Minnesota Supercomputing Institute, University of Minnesota
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Paul Reiners
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Anders J Perrone
- Masonic Institute for the Developing Brain, University of Minnesota
| | | | - Erik G Lee
- Minnesota Supercomputing Institute, University of Minnesota
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Martin Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill
| | - Omid Kardan
- Department of Psychology, University of Chicago
- University of Michigan
| | | | | | | | | | - Sally Stoyell
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Tabitha Martin
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Sooyeon Sung
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Ermias Fair
- Masonic Institute for the Developing Brain, University of Minnesota
| | | | | | - Essa Yacoub
- Department of Radiology, University of Minnesota
- Center for Magnetic Resonance Research, University of Minnesota
| | | | | | - Jed T Elison
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
- Department of Pediatrics, University of Minnesota
| | | | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
- Department of Pediatrics, University of Minnesota
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota
- Department of Pediatrics, University of Minnesota
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36
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Ho JK, Horikawa T, Majima K, Cheng F, Kamitani Y. Inter-individual deep image reconstruction via hierarchical neural code conversion. Neuroimage 2023; 271:120007. [PMID: 36914105 DOI: 10.1016/j.neuroimage.2023.120007] [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: 12/29/2022] [Revised: 02/26/2023] [Accepted: 03/07/2023] [Indexed: 03/13/2023] Open
Abstract
The sensory cortex is characterized by general organizational principles such as topography and hierarchy. However, measured brain activity given identical input exhibits substantially different patterns across individuals. Although anatomical and functional alignment methods have been proposed in functional magnetic resonance imaging (fMRI) studies, it remains unclear whether and how hierarchical and fine-grained representations can be converted between individuals while preserving the encoded perceptual content. In this study, we trained a method of functional alignment called neural code converter that predicts a target subject's brain activity pattern from a source subject given the same stimulus, and analyzed the converted patterns by decoding hierarchical visual features and reconstructing perceived images. The converters were trained on fMRI responses to identical sets of natural images presented to pairs of individuals, using the voxels on the visual cortex that covers from V1 through the ventral object areas without explicit labels of the visual areas. We decoded the converted brain activity patterns into the hierarchical visual features of a deep neural network using decoders pre-trained on the target subject and then reconstructed images via the decoded features. Without explicit information about the visual cortical hierarchy, the converters automatically learned the correspondence between visual areas of the same levels. Deep neural network feature decoding at each layer showed higher decoding accuracies from corresponding levels of visual areas, indicating that hierarchical representations were preserved after conversion. The visual images were reconstructed with recognizable silhouettes of objects even with relatively small numbers of data for converter training. The decoders trained on pooled data from multiple individuals through conversions led to a slight improvement over those trained on a single individual. These results demonstrate that the hierarchical and fine-grained representation can be converted by functional alignment, while preserving sufficient visual information to enable inter-individual visual image reconstruction.
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Affiliation(s)
- Jun Kai Ho
- Graduate School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan.
| | - Tomoyasu Horikawa
- Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Hikaridai, Seika, Soraku, Kyoto, 619-0288, Japan
| | - Kei Majima
- Graduate School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan
| | - Fan Cheng
- Graduate School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan; Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Hikaridai, Seika, Soraku, Kyoto, 619-0288, Japan
| | - Yukiyasu Kamitani
- Graduate School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan; Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Hikaridai, Seika, Soraku, Kyoto, 619-0288, Japan.
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Hauptman M, Blank I, Fedorenko E. Non-literal language processing is jointly supported by the language and theory of mind networks: Evidence from a novel meta-analytic fMRI approach. Cortex 2023; 162:96-114. [PMID: 37023480 PMCID: PMC10210011 DOI: 10.1016/j.cortex.2023.01.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/08/2022] [Accepted: 01/11/2023] [Indexed: 03/12/2023]
Abstract
Going beyond the literal meaning of language is key to communicative success. However, the mechanisms that support non-literal inferences remain debated. Using a novel meta-analytic approach, we evaluate the contribution of linguistic, social-cognitive, and executive mechanisms to non-literal interpretation. We identified 74 fMRI experiments (n = 1,430 participants) from 2001 to 2021 that contrasted non-literal language comprehension with a literal control condition, spanning ten phenomena (e.g., metaphor, irony, indirect speech). Applying the activation likelihood estimation approach to the 825 activation peaks yielded six left-lateralized clusters. We then evaluated the locations of both the individual-study peaks and the clusters against probabilistic functional atlases (cf. anatomical locations, as is typically done) for three candidate brain networks-the language-selective network (Fedorenko, Behr, & Kanwisher, 2011), which supports language processing, the Theory of Mind (ToM) network (Saxe & Kanwisher, 2003), which supports social inferences, and the domain-general Multiple-Demand (MD) network (Duncan, 2010), which supports executive control. These atlases were created by overlaying individual activation maps of participants who performed robust and extensively validated 'localizer' tasks that selectively target each network in question (n = 806 for language; n = 198 for ToM; n = 691 for MD). We found that both the individual-study peaks and the ALE clusters fell primarily within the language network and the ToM network. These results suggest that non-literal processing is supported by both i) mechanisms that process literal linguistic meaning, and ii) mechanisms that support general social inference. They thus undermine a strong divide between literal and non-literal aspects of language and challenge the claim that non-literal processing requires additional executive resources.
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Affiliation(s)
- Miriam Hauptman
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Idan Blank
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Department of Psychology, UCLA, Los Angeles, CA 90095, USA; Department of Linguistics, UCLA, Los Angeles, CA 90095, USA
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Program in Speech and Hearing in Bioscience and Technology, Harvard University, Boston, MA 02114, USA.
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38
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Bedini M, Olivetti E, Avesani P, Baldauf D. Accurate localization and coactivation profiles of the frontal eye field and inferior frontal junction: an ALE and MACM fMRI meta-analysis. Brain Struct Funct 2023; 228:997-1017. [PMID: 37093304 PMCID: PMC10147761 DOI: 10.1007/s00429-023-02641-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 04/08/2023] [Indexed: 04/25/2023]
Abstract
The frontal eye field (FEF) and the inferior frontal junction (IFJ) are prefrontal structures involved in mediating multiple aspects of goal-driven behavior. Despite being recognized as prominent nodes of the networks underlying spatial attention and oculomotor control, and working memory and cognitive control, respectively, the limited quantitative evidence on their precise localization has considerably impeded the detailed understanding of their structure and connectivity. In this study, we performed an activation likelihood estimation (ALE) fMRI meta-analysis by selecting studies that employed standard paradigms to accurately infer the localization of these regions in stereotaxic space. For the FEF, we found the highest spatial convergence of activations for prosaccade and antisaccade paradigms at the junction of the precentral sulcus and superior frontal sulcus. For the IFJ, we found consistent activations across oddball/attention, working memory, task-switching and Stroop paradigms at the junction of the inferior precentral sulcus and inferior frontal sulcus. We related these clusters to previous meta-analyses, sulcal/gyral neuroanatomy, and a comprehensive brain parcellation, highlighting important differences compared to their results and taxonomy. Finally, we leveraged the ALE peak coordinates as seeds to perform a meta-analytic connectivity modeling (MACM) analysis, which revealed systematic coactivation patterns spanning the frontal, parietal, and temporal cortices. We decoded the behavioral domains associated with these coactivations, suggesting that these may allow FEF and IFJ to support their specialized roles in flexible behavior. Our study provides the meta-analytic groundwork for investigating the relationship between functional specialization and connectivity of two crucial control structures of the prefrontal cortex.
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Affiliation(s)
- Marco Bedini
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy.
- Department of Psychology, University of California, San Diego, McGill Hall 9500 Gilman Dr, La Jolla, CA, 92093-0109, USA.
| | - Emanuele Olivetti
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy
- NILab, Bruno Kessler Foundation (FBK), Via delle Regole 101, 38123, Trento, Italy
| | - Paolo Avesani
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy
- NILab, Bruno Kessler Foundation (FBK), Via delle Regole 101, 38123, Trento, Italy
| | - Daniel Baldauf
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy
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da Rocha JLD, Kepinska O, Schneider P, Benner J, Degano G, Schneider L, Golestani N. Multivariate Concavity Amplitude Index (MCAI) for characterizing Heschl's gyrus shape. Neuroimage 2023; 272:120052. [PMID: 36965861 DOI: 10.1016/j.neuroimage.2023.120052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 03/21/2023] [Accepted: 03/22/2023] [Indexed: 03/27/2023] Open
Abstract
Heschl's gyrus (HG), which includes primary auditory cortex, is highly variable in its shape (i.e. gyrification patterns), between hemispheres and across individuals. Differences in HG shape have been observed in the context of phonetic learning skill and expertise, and of professional musicianship, among others. Two of the most common configurations of HG include single HG, where a single transverse temporal gyrus is present, and common stem duplications (CSD), where a sulcus intermedius (SI) arises from the lateral aspect of HG. Here we describe a new toolbox, called 'Multivariate Concavity Amplitude Index' (MCAI), which automatically assesses the shape of HG. MCAI works on the output of TASH, our first toolbox which automatically segments HG, and computes continuous indices of concavity, which arise when sulci are present, along the outer perimeter of an inflated representation of HG, in a directional manner. Thus, MCAI provides a multivariate measure of shape, which is reproducible and sensitive to small variations in shape. We applied MCAI to structural magnetic resonance imaging (MRI) data of N=181 participants, including professional and amateur musicians and from non-musicians. Former studies have shown large variations in HG shape in the former groups. We validated MCAI by showing high correlations between the dominant (i.e. highest) lateral concavity values and continuous visual assessments of the degree of lateral gyrification of the first gyrus. As an application of MCAI, we also replicated previous visually obtained findings showing a higher likelihood of bilateral CSDs in musicians. MCAI opens a wide range of applications in evaluating HG shape in the context of individual differences, expertise, disorder and genetics.
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Affiliation(s)
- Josué Luiz Dalboni da Rocha
- Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, USA; Brain and Language Lab, Department of Psychology, Faculty of Psychology and Educational Sciences, University of Geneva, Switzerland.
| | - Olga Kepinska
- Brain and Language Lab, Cognitive Science Hub, University of Vienna, Vienna, Austria; Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Peter Schneider
- Department of Neuroradiology and Department of Neurology, Section of Biomagnetism, University of Heidelberg Hospital, Heidelberg, Germany; Centre for Systematic Musicology, University of Graz, Graz, Austria; Vitols Jazeps Latvian Academy of Music, Riga, Latvia
| | - Jan Benner
- Department of Neuroradiology and Department of Neurology, Section of Biomagnetism, University of Heidelberg Hospital, Heidelberg, Germany
| | - Giulio Degano
- Brain and Language Lab, Department of Psychology, Faculty of Psychology and Educational Sciences, University of Geneva, Switzerland
| | - Letitia Schneider
- Brain and Language Lab, Cognitive Science Hub, University of Vienna, Vienna, Austria
| | - Narly Golestani
- Brain and Language Lab, Department of Psychology, Faculty of Psychology and Educational Sciences, University of Geneva, Switzerland; Brain and Language Lab, Cognitive Science Hub, University of Vienna, Vienna, Austria; Department of Behavioral and Cognitive Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria; Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
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Gallardo G, Eichner C, Sherwood CC, Hopkins WD, Anwander A, Friederici AD. Uncovering the Morphological Evolution of Language-Relevant Brain Areas. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.17.533103. [PMID: 36993711 PMCID: PMC10055248 DOI: 10.1101/2023.03.17.533103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Human language is supported by a cortical network involving Broca's area which comprises Brodmann Areas 44 and 45 (BA44, BA45). While cytoarchitectonic homolog areas have been identified in nonhuman primates, it remains unknown how these regions evolved to support human language. Here, we use histological data and advanced cortical registration methods to precisely compare the morphology of BA44 and 45 between humans and chimpanzees. We found a general expansion of Broca's areas in humans, with the left BA44 enlarging the most, growing anteriorly into a region known to process syntax. Together with recent functional studies, our findings show that BA44 evolved from a purely action-related region to a more expanded region in humans, with a posterior portion supporting action and an anterior portion supporting syntactic processes. Furthermore, our findings provide a solution for the longstanding debate concerning the structural and functional evolution of Broca's area and its role in action and language.
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Affiliation(s)
- Guillermo Gallardo
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Cornelius Eichner
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Chet C. Sherwood
- Department of Anthropology, The George Washington University, Washington DC, USA
| | - William D. Hopkins
- Department of Comparative Medicine, Michale E. Keeling Center for Comparative Medicine, The University of Texas MD Anderson Cancer Center, Bastrop, Texas, USA
| | - Alfred Anwander
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Angela D. Friederici
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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41
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Novek J, Sprung-Much T, Nolan E, Petrides M. Optimal blocking of the cerebral cortex for cytoarchitectonic examination: a neuronavigation-based approach. Cereb Cortex 2023; 33:2704-2714. [PMID: 35780434 DOI: 10.1093/cercor/bhac236] [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: 10/28/2021] [Revised: 05/11/2022] [Accepted: 05/12/2021] [Indexed: 11/13/2022] Open
Abstract
Certain sulci of the human cerebral cortex hold consistent relationships to cytoarchitectonic areas (e.g. the primary motor cortical area 4 and the somatosensory cortical area 3 occupy the anterior and posterior banks of the central sulcus, respectively). Recent research has improved knowledge of the cortical sulci and their variability across individuals. However, other than the so-called primary sulci, understanding of the precise relationships cortical folds hold with many cytoarchitectonic areas remains elusive. To examine these relationships, the cortex must be blocked, sectioned, and histologically processed in a manner that allows the cytoarchitectonic layers to be clearly observed. The optimal strategy to view the cytoarchitecture is to block and section the cortex perpendicular to the sulcal orientation. Most cytoarchitectonic investigations of the cortex, however, have been conducted on specimens cut along a single axis (e.g. the coronal plane), which distorts the appearance of the cytoarchitectonic layers within parts of the cortical ribbon not sectioned optimally. Thus, to understand further the relationships between sulci and cytoarchitectonic areas, the cortex should be sectioned optimally to the sulci of interest. A novel approach for blocking the cortex optimally using structural magnetic resonance imaging (MRI) and surgical neuronavigation tools is presented here.
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Affiliation(s)
- Jennifer Novek
- Montreal Neurological Institute, McGill University, 3801 University, Montreal, QC, Canada, H3A 2B4
| | - Trisanna Sprung-Much
- Montreal Neurological Institute, McGill University, 3801 University, Montreal, QC, Canada, H3A 2B4
| | - Erika Nolan
- Montreal Neurological Institute, McGill University, 3801 University, Montreal, QC, Canada, H3A 2B4
| | - Michael Petrides
- Montreal Neurological Institute, McGill University, 3801 University, Montreal, QC, Canada, H3A 2B4
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42
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Cao C, Li Y, Zhang L, Hu F, Gao X. Identification for the cortical 3-Hinges folding pattern based on cortical morphological and structural features. Front Neurosci 2023; 17:1125666. [PMID: 36968484 PMCID: PMC10034048 DOI: 10.3389/fnins.2023.1125666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
The Cortical 3-Hinges Folding Pattern (i.e., 3-Hinges) is one of the brain's hallmarks, and it is of great reference for predicting human intelligence, diagnosing eurological diseases and understanding the brain functional structure differences among gender. Given the significant morphological variability among individuals, it is challenging to identify 3-Hinges, but current 3-Hinges researches are mainly based on the computationally expensive Gyral-net method. To address this challenge, this paper aims to develop a deep network model to realize the fast identification of 3-Hinges based on cortical morphological and structural features. The main work includes: (1) The morphological and structural features of the cerebral cortex are extracted to relieve the imbalance between the number of 3-Hinges and each brain image's voxels; (2) The feature vector is constructed with the K nearest neighbor algorithm from the extracted scattered features of the morphological and structural features to alleviate over-fitting in training; (3) The squeeze excitation module combined with the deep U-shaped network structure is used to learn the correlation of the channels among the feature vectors; (4) The functional structure roles that 3-Hinges plays between adolescent males and females are discussed in this work. The experimental results on both adolescent and adult MRI datasets show that the proposed model achieves better performance in terms of time consumption. Moreover, this paper reveals that cortical sulcus information plays a critical role in the procedure of identification, and the cortical thickness, cortical surface area, and volume characteristics can supplement valuable information for 3-Hinges identification to some extent. Furthermore, there are significant structural differences on 3-Hinges among adolescent gender.
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Affiliation(s)
- Chunhong Cao
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, China
| | - Yongquan Li
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, China
| | - Lele Zhang
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, China
| | - Fang Hu
- Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou, China
- *Correspondence: Fang Hu
| | - Xieping Gao
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China
- Xieping Gao
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Haenelt D, Trampel R, Nasr S, Polimeni JR, Tootell RBH, Sereno MI, Pine KJ, Edwards LJ, Helbling S, Weiskopf N. High-resolution quantitative and functional MRI indicate lower myelination of thin and thick stripes in human secondary visual cortex. eLife 2023; 12:e78756. [PMID: 36888685 PMCID: PMC9995117 DOI: 10.7554/elife.78756] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 02/08/2023] [Indexed: 03/09/2023] Open
Abstract
The characterization of cortical myelination is essential for the study of structure-function relationships in the human brain. However, knowledge about cortical myelination is largely based on post-mortem histology, which generally renders direct comparison to function impossible. The repeating pattern of pale-thin-pale-thick stripes of cytochrome oxidase (CO) activity in the primate secondary visual cortex (V2) is a prominent columnar system, in which histology also indicates different myelination of thin/thick versus pale stripes. We used quantitative magnetic resonance imaging (qMRI) in conjunction with functional magnetic resonance imaging (fMRI) at ultra-high field strength (7 T) to localize and study myelination of stripes in four human participants at sub-millimeter resolution in vivo. Thin and thick stripes were functionally localized by exploiting their sensitivity to color and binocular disparity, respectively. Resulting functional activation maps showed robust stripe patterns in V2 which enabled further comparison of quantitative relaxation parameters between stripe types. Thereby, we found lower longitudinal relaxation rates (R1) of thin and thick stripes compared to surrounding gray matter in the order of 1-2%, indicating higher myelination of pale stripes. No consistent differences were found for effective transverse relaxation rates (R2*). The study demonstrates the feasibility to investigate structure-function relationships in living humans within one cortical area at the level of columnar systems using qMRI.
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Affiliation(s)
- Daniel Haenelt
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- International Max Planck Research School on Neuroscience of Communication: Function, Structure, and PlasticityLeipzigGermany
| | - Robert Trampel
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Shahin Nasr
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownUnited States
- Department of Radiology, Harvard Medical SchoolBostonUnited States
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownUnited States
- Department of Radiology, Harvard Medical SchoolBostonUnited States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Roger BH Tootell
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownUnited States
- Department of Radiology, Harvard Medical SchoolBostonUnited States
| | - Martin I Sereno
- Department of Psychology, College of Sciences, San Diego State UniversitySan DiegoUnited States
| | - Kerrin J Pine
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Luke J Edwards
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Saskia Helbling
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Poeppel Lab, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck SocietyFrankfurt am MainGermany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig UniversityLeipzigGermany
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Zachlod D, Palomero-Gallagher N, Dickscheid T, Amunts K. Mapping Cytoarchitectonics and Receptor Architectonics to Understand Brain Function and Connectivity. Biol Psychiatry 2023; 93:471-479. [PMID: 36567226 DOI: 10.1016/j.biopsych.2022.09.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/18/2022] [Accepted: 09/10/2022] [Indexed: 02/04/2023]
Abstract
This review focuses on cytoarchitectonics and receptor architectonics as biological correlates of function and connectivity. It introduces the 3-dimensional cytoarchitectonic probabilistic maps of cortical areas and nuclei of the Julich-Brain Atlas, available at EBRAINS, to study structure-function relationships. The maps are linked to the BigBrain as microanatomical reference model and template space. The siibra software tool suite enables programmatic access to the maps and to receptor architectonic data that are anchored to brain areas. Such cellular and molecular data are tools for studying magnetic resonance connectivity including modeling and simulation. At the end, we highlight perspectives of the Julich-Brain as well as methodological considerations. Thus, microstructural maps as part of a multimodal atlas help elucidate the biological correlates of large-scale networks and brain function with a high level of anatomical detail, which provides a basis to study brains of patients with psychiatric disorders.
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Affiliation(s)
- Daniel Zachlod
- Institute of Neurosciences and Medicine, Research Centre Jülich, Jülich, Germany.
| | - Nicola Palomero-Gallagher
- Institute of Neurosciences and Medicine, Research Centre Jülich, Jülich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Department of Psychiatry, Psychotherapy, Psychosomatics, Medical Faculty, RWTH Aachen, Jülich Aachen Research Alliance-Translational Brain Medicine, Aachen, Germany
| | - Timo Dickscheid
- Institute of Neurosciences and Medicine, Research Centre Jülich, Jülich, Germany; Helmholtz AI, Research Centre Jülich, Jülich, Germany; Department of Computer Science, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Katrin Amunts
- Institute of Neurosciences and Medicine, Research Centre Jülich, Jülich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
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45
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Moinian S, Vegh V, Reutens D. Towards automated in vivo parcellation of the human cerebral cortex using supervised classification of magnetic resonance fingerprinting residuals. Cereb Cortex 2023; 33:1550-1565. [PMID: 35483706 PMCID: PMC9977388 DOI: 10.1093/cercor/bhac155] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Accurate parcellation of the cerebral cortex in an individual is a guide to its underlying organization. The most promising in vivo quantitative magnetic resonance (MR)-based microstructural cortical mapping methods are yet to achieve a level of parcellation accuracy comparable to quantitative histology. METHODS We scanned 6 participants using a 3D echo-planar imaging MR fingerprinting (EPI-MRF) sequence on a 7T Siemens scanner. After projecting MRF signals to the individual-specific inflated model of the cortical surface, normalized autocorrelations of MRF residuals of vertices of 8 microstructurally distinct areas (BA1, BA2, BA4a, BA6, BA44, BA45, BA17, and BA18) from 3 cortical regions were used as feature vector inputs into linear support vector machine (SVM), radial basis function SVM (RBF-SVM), random forest, and k-nearest neighbors supervised classification algorithms. The algorithms' prediction performance was compared using: (i) features from each vertex or (ii) features from neighboring vertices. RESULTS The neighborhood-based RBF-SVM classifier achieved the highest prediction score of 0.85 for classification of MRF residuals in the central region from a held-out participant. CONCLUSIONS We developed an automated method of cortical parcellation using a combination of MR fingerprinting residual analysis and machine learning classification. Our findings provide the basis for employing unsupervised learning algorithms for whole-cortex structural parcellation in individuals.
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Affiliation(s)
- Shahrzad Moinian
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
| | - Viktor Vegh
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
| | - David Reutens
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
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46
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Cheng Q, Roth A, Halgren E, Klein D, Chen JK, Mayberry RI. Restricted language access during childhood affects adult brain structure in selective language regions. Proc Natl Acad Sci U S A 2023; 120:e2215423120. [PMID: 36745780 PMCID: PMC9963327 DOI: 10.1073/pnas.2215423120] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 01/04/2023] [Indexed: 02/08/2023] Open
Abstract
Due to the ubiquitous nature of language in the environment of infants, how it affects the anatomical structure of the brain language system over the lifespan is not well understood. In this study, we investigated the effects of early language experience on the adult brain by examining anatomical features of individuals born deaf with typical or restricted language experience in early childhood. Twenty-two deaf adults whose primary language was American Sign Language and were first immersed in it at ages ranging from birth to 14 y participated. The control group was 21 hearing non-signers. We acquired T1-weighted magnetic resonance images and used FreeSurfer [B. Fischl, Neuroimage 62, 774-781(2012)] to reconstruct the brain surface. Using an a priori regions of interest (ROI) approach, we identified 17 language and 19 somatomotor ROIs in each hemisphere from the Human Connectome Project parcellation map [M. F. Glasser et al., Nature 536, 171-178 (2016)]. Restricted language experience in early childhood was associated with negative changes in adjusted grey matter volume and/or cortical thickness in bilateral fronto-temporal regions. No evidence of anatomical differences was observed in any of these regions when deaf signers with infant sign language experience were compared with hearing speakers with infant spoken language experience, showing that the effects of early language experience on the brain language system are supramodal.
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Affiliation(s)
- Qi Cheng
- Department of Linguistics, University of Washington, Seattle, WA98195
| | - Austin Roth
- Department of Linguistics, University of California San Diego, San Diego, CA92093
| | - Eric Halgren
- Department of Radiology, University of California San Diego, San Diego, CA92093
- Department of Neuroscience, University of California San Diego, San Diego, CA92093
| | - Denise Klein
- Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, MontrealH3A 2B4Canada
| | - Jen-Kai Chen
- Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, MontrealH3A 2B4Canada
| | - Rachel I. Mayberry
- Department of Linguistics, University of California San Diego, San Diego, CA92093
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47
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Krijnen EA, Ngamsombat C, George IC, Yu FF, Fan Q, Tian Q, Huang SY, Klawiter EC. Axonal and myelin changes and their inter-relationship in the optic radiations in people with multiple sclerosis. Mult Scler J Exp Transl Clin 2023; 9:20552173221147620. [PMID: 36814811 PMCID: PMC9940187 DOI: 10.1177/20552173221147620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023] Open
Abstract
Background The imaging g-ratio, estimated from axonal volume fraction (AVF) and myelin volume fraction (MVF), is a novel biomarker of microstructural tissue integrity in multiple sclerosis (MS). Objective To assess axonal and myelin changes and their inter-relationship as measured by g-ratio in the optic radiations (OR) in people with MS (pwMS) with and without previous optic neuritis (ON) compared to healthy controls (HC). Methods Thirty pwMS and 17 HCs were scanned on a 3Tesla Connectom scanner. AVF and MVF, derived from a multi-shell diffusion protocol and macromolecular tissue volume, respectively, were measured in normal-appearing white matter (NAWM) and lesions within the OR and used to calculate imaging g-ratio. Results OR AVF and MVF were decreased in pwMS compared to HC, and in OR lesions compared to NAWM, whereas the g-ratio was not different. Compared to pwMS with previous ON, AVF and g-ratio tended to be higher in pwMS without prior ON. AVF and MVF, particularly in NAWM, were positively correlated with retinal thickness, which was more pronounced in pwMS with prior ON. Conclusion Axonal measures reflect microstructural tissue damage in the OR, particularly in the setting of remote ON, and correlate with established metrics of visual health in MS.
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Affiliation(s)
- Eva A Krijnen
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chanon Ngamsombat
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Ilena C George
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Fang F Yu
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qiuyun Fan
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Academy of Medical Engineering and Translational Medicine, Medical College, Tianjin University, Tianjin, China
| | - Qiyuan Tian
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Susie Y Huang
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Eric C Klawiter
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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48
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Sarbisheh I, Tapak L, Fallahi A, Fardmal J, Sadeghifar M, Nazemzadeh M, Mehvari Habibabadi J. Cortical thickness analysis in temporal lobe epilepsy using fully Bayesian spectral method in magnetic resonance imaging. BMC Med Imaging 2022; 22:222. [PMID: 36544100 PMCID: PMC9768883 DOI: 10.1186/s12880-022-00949-5] [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: 05/19/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Temporal lobe epilepsy (TLE) is the most common type of epilepsy associated with changes in the cerebral cortex throughout the brain. Magnetic resonance imaging (MRI) is widely used for detecting such anomalies; nevertheless, it produces spatially correlated data that cannot be considered by the usual statistical models. This study aimed to compare cortical thicknesses between patients with TLE and healthy controls by considering the spatial dependencies across different regions of the cerebral cortex in MRI. METHODS In this study, T1-weighted MRI was performed on 20 healthy controls and 33 TLE patients. Nineteen patients had a left TLE and 14 had a right TLE. Cortical thickness was measured for all individuals in 68 regions of the cerebral cortex based on images. Fully Bayesian spectral method was utilized to compare the cortical thickness of different brain regions between groups. Neural networks model was used to classify the patients using the identified regions. RESULTS For the left TLE patients, cortical thinning was observed in bilateral caudal anterior cingulate, lateral orbitofrontal (ipsilateral), the bilateral rostral anterior cingulate, frontal pole and temporal pole (ipsilateral), caudal middle frontal and rostral middle frontal (contralateral side). For the right TLE patients, cortical thinning was only observed in the entorhinal area (ipsilateral). The AUCs of the neural networks for classification of left and right TLE patients versus healthy controls were 0.939 and 1.000, respectively. CONCLUSION Alteration of cortical gray matter thickness was evidenced as common effect of epileptogenicity, as manifested by the patients in this study using the fully Bayesian spectral method by taking into account the complex structure of the data.
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Affiliation(s)
- Iman Sarbisheh
- grid.411950.80000 0004 0611 9280Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Leili Tapak
- grid.411950.80000 0004 0611 9280Department of Biostatistics, School of Public Health and Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Alireza Fallahi
- grid.411705.60000 0001 0166 0922Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Instruments Institute (AMTII), Tehran University of Medical Sciences, Tehran, Iran ,grid.459564.f0000 0004 0482 9174Biomedical Engineering Department, Hamedan University of Technology, Hamedan, Iran
| | - Javad Fardmal
- grid.411950.80000 0004 0611 9280Department of Biostatistics, School of Public Health and Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Majid Sadeghifar
- grid.411807.b0000 0000 9828 9578Department of Statistics, Faculty of Science, Bu-Ali Sina University, Hamadan, Iran
| | - MohammadReza Nazemzadeh
- grid.411705.60000 0001 0166 0922Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Instruments Institute (AMTII), Tehran University of Medical Sciences, Tehran, Iran ,grid.411705.60000 0001 0166 0922Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran
| | - Jafar Mehvari Habibabadi
- grid.411036.10000 0001 1498 685XDepartment of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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49
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Zhang L, Zhao L, Liu D, Wu Z, Wang X, Liu T, Zhu D. Cortex2vector: anatomical embedding of cortical folding patterns. Cereb Cortex 2022; 33:5851-5862. [PMID: 36487182 PMCID: PMC10183757 DOI: 10.1093/cercor/bhac465] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/01/2022] [Accepted: 11/02/2022] [Indexed: 12/13/2022] Open
Abstract
Abstract
Current brain mapping methods highly depend on the regularity, or commonality, of anatomical structure, by forcing the same atlas to be matched to different brains. As a result, individualized structural information can be overlooked. Recently, we conceptualized a new type of cortical folding pattern called the 3-hinge gyrus (3HG), which is defined as the conjunction of gyri coming from three directions. Many studies have confirmed that 3HGs are not only widely existing on different brains, but also possess both common and individual patterns. In this work, we put further effort, based on the identified 3HGs, to establish the correspondences of individual 3HGs. We developed a learning-based embedding framework to encode individual cortical folding patterns into a group of anatomically meaningful embedding vectors (cortex2vector). Each 3HG can be represented as a combination of these embedding vectors via a set of individual specific combining coefficients. In this way, the regularity of folding pattern is encoded into the embedding vectors, while the individual variations are preserved by the multi-hop combination coefficients. Results show that the learned embeddings can simultaneously encode the commonality and individuality of cortical folding patterns, as well as robustly infer the complicated many-to-many anatomical correspondences among different brains.
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Affiliation(s)
- Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington , Arlington, 76010, USA
| | - Lin Zhao
- Department of Computer Science, The University of Georgia , Athens, 30602, USA
| | | | - Zihao Wu
- Department of Computer Science, The University of Georgia , Athens, 30602, USA
| | - Xianqiao Wang
- College of Engineering, The University of Georgia , Athens, 30602, USA
| | - Tianming Liu
- Department of Computer Science, The University of Georgia , Athens, 30602, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington , Arlington, 76010, USA
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50
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Alves PN, Forkel SJ, Corbetta M, Thiebaut de Schotten M. The subcortical and neurochemical organization of the ventral and dorsal attention networks. Commun Biol 2022; 5:1343. [PMID: 36477440 PMCID: PMC9729227 DOI: 10.1038/s42003-022-04281-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
Attention is a core cognitive function that filters and selects behaviourally relevant information in the environment. The cortical mapping of attentional systems identified two segregated networks that mediate stimulus-driven and goal-driven processes, the Ventral and the Dorsal Attention Networks (VAN, DAN). Deep brain electrophysiological recordings, behavioral data from phylogenetic distant species, and observations from human brain pathologies challenge purely corticocentric models. Here, we used advanced methods of functional alignment applied to resting-state functional connectivity analyses to map the subcortical architecture of the Ventral and Dorsal Attention Networks. Our investigations revealed the involvement of the pulvinar, the superior colliculi, the head of caudate nuclei, and a cluster of brainstem nuclei relevant to both networks. These nuclei are densely connected structural network hubs, as revealed by diffusion-weighted imaging tractography. Their projections establish interrelations with the acetylcholine nicotinic receptor as well as dopamine and serotonin transporters, as demonstrated in a spatial correlation analysis with a normative atlas of neurotransmitter systems. This convergence of functional, structural, and neurochemical evidence provides a comprehensive framework to understand the neural basis of attention across different species and brain diseases.
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Affiliation(s)
- Pedro Nascimento Alves
- grid.9983.b0000 0001 2181 4263Laboratório de Estudos de Linguagem, Centro de Estudos Egas Moniz, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal ,grid.411265.50000 0001 2295 9747Serviço de Neurologia, Departmento de Neurociências e Saúde Mental, Hospital de Santa Maria, CHULN, Lisboa, Portugal
| | - Stephanie J. Forkel
- grid.462844.80000 0001 2308 1657Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France ,grid.5590.90000000122931605Donders Institute for Brain Cognition Behaviour, Radboud University, Thomas van Aquinostraat 4, 6525GD Nijmegen, the Netherlands ,grid.13097.3c0000 0001 2322 6764Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK ,grid.6936.a0000000123222966Departments of Neurosurgery, Technical University of Munich School of Medicine, Munich, Germany
| | - Maurizio Corbetta
- grid.5608.b0000 0004 1757 3470Clinica Neurologica, Department of Neuroscience, University of Padova, Padova, Italy ,grid.5608.b0000 0004 1757 3470Padova Neuroscience Center (PNC), University of Padova, Padova, Italy ,grid.428736.cVenetian Institute of Molecular Medicine, VIMM, Padova, Italy ,grid.4367.60000 0001 2355 7002Department of Neurology, Radiology, Neuroscience Washington University School of Medicine, St.Louis, MO USA
| | - Michel Thiebaut de Schotten
- grid.462844.80000 0001 2308 1657Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France ,grid.412041.20000 0001 2106 639XGroupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France
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