scholarly journals Big and Small Cerebral Asymmetries

2018 ◽  
Author(s):  
Mark A. Eckert ◽  
Kenneth I. Vaden ◽  
Keyword(s):  
White Matter ◽  
Functional Significance ◽  
Grey Matter ◽  
Brain Regions ◽  
Cerebral Asymmetries ◽  
Deformation Parameters ◽  
Increased Sensitivity ◽  
Scale Normalization ◽  
Asymmetry Measures

Letter HighlightsDeformation-based asymmetries replicate previously observed grey matter asymmetries and characterize white matter asymmetries.Increased sensitivity to structural asymmetries in some brain regions depends on smaller-scale normalization or deformation parameters.Tuning deformation parameters can provide more precise asymmetry measures for understanding the mechanisms and functional significance of cerebral asymmetries.

scholarly journals Late chronotype is linked to greater cortical thickness in the left fusiform and entorhinal gyri

2021 ◽  
Author(s):  
Michal Rafal Zareba ◽  
Magdalena Fafrowicz ◽  
Tadeusz Marek ◽  
Ewa Beldzik ◽  
Halszka Oginska ◽  
...  
Keyword(s):  
White Matter ◽  
Cortical Thickness ◽  
Grey Matter ◽  
Brain Regions ◽  
Affective Processing ◽  
Grey Matter Volume ◽  
Imaging Data ◽  
White Matter Volume ◽  
Matter Volume ◽  
Anatomical Basis

Abstract Humans can be classified as early, intermediate and late chronotypes based on the preferred sleep and wakefulness patterns. The anatomical basis of these distinctions remains largely unexplored. Using magnetic resonance imaging data from 113 healthy young adults (71 females), we aimed to replicate cortical thickness and grey matter volume chronotype differences reported earlier in the literature using a greater sample size, as well as to explore the volumetric white matter variation linked to contrasting circadian phenotypes. Instead of comparing the chronotypes, we correlated the individual chronotype scores with their morphometric brain measures. The results revealed one cluster in the left fusiform and entorhinal gyri showing increased cortical thickness with increasing preference for eveningness, potentially providing an anatomical substrate for chronotype-sensitive affective processing. No significant results were found for grey and white matter volume. We failed to replicate cortical thickness and volumetric grey matter distinctions in the brain regions reported in the literature. Furthermore, we found no association between white matter volume and chronotype. Thus, while this study confirms that circadian preference is associated with specific structural substrates, it adds to the growing concerns that reliable and replicable neuroimaging research requires datasets much larger than those commonly used.

scholarly journals Modelling the Anatomical Distribution of Neurological Events in COVID-19 Patients: A Systematic Review

2020 ◽  
Author(s):  
Nicholas Parsons ◽  
Athanasia Outsikas ◽  
Annie Parish ◽  
Rebecca Clohesy ◽  
Nilam Thakkar ◽  
...  
Keyword(s):  
Systematic Review ◽  
Spatial Distribution ◽  
White Matter ◽  
Diffusion Model ◽  
Grey Matter ◽  
Brain Regions ◽  
Neurological Symptoms ◽  
Linear Diffusion ◽  
Spread Model ◽  
Network Diffusion

SummaryBackgroundNeuropathology caused by the coronavirus disease 2019 (COVID-19) has been reported across several studies. The characterisation of the spatial distribution of these pathology remains critical to assess long and short-term neurological sequelae of COVID-19. To this end, Mathematical models can be used to characterise the location and aetiologies underlying COVID-19-related neuropathology.MethodWe performed a systematic review of the literature to quantify the locations of small neurological events identified with magnetic resonance imaging (MRI) among COVID-19 patients. Neurological events were localised into the Desikan-Killiany grey and white matter atlases. A mathematical network diffusion model was then used to test whether the spatial distribution of neurological events could be explained via a linear spread through the structural connectome of the brain.FindingsWe identified 35 articles consisting of 123 patients that assessed the spatial distribution of small neurological events among COVID-19 patients. Of these, 91 patients had grey matter changes, 95 patients had white matter changes and 72 patients had confirmed cerebral microbleeds. White matter events were observed within 14 of 42 white matter bundles from the IIT atlas. The highest proportions (26%) of events were observed within the bilateral corticospinal tracts. The splenium and middle of the corpus callosum were affected in 14% and 9% of the cases respectively. Grey matter events were spatially distributed in the 41 brain regions within the Desikan-Killiany atlas. The highest proportions (∼10%) of the events were observed in areas including the bilateral superior temporal, precentral, and lateral occipital cortices. Sub-cortical events were most frequently identified in the Pallidum. The application of a mathematical network diffusion model suggested that the spatial pattern of the small neurological events in COVID-19 can be modelled with a linear diffusion of spread from epicentres in the bilateral cerebellum and basal ganglia (Pearson’s r=0.41, p<0.001, corrected).InterpretationTo our knowledge, this is the first study to systematically characterise the spatial distribution of small neurological events in COVID-19 patients and test whether the spatial distribution of these events can be explained by a linear diffusion spread model. The location of neurological events is consistent with commonly identified neurological symptoms including alterations in conscious state among COVID-19 patients that require brain imaging. Given the prevalence and severity of these manifestations, clinicians should carefully monitor neurological symptoms within COVID-19 patients and their potential long-term sequelae.

scholarly journals Susceptibility to capillary plugging can predict brain region specific vessel loss with aging

2020 ◽  
Vol 40 (12) ◽  
pp. 2475-2490 ◽  
Author(s):  
Ben Schager ◽  
Craig E Brown
Keyword(s):  
Visual Cortex ◽  
White Matter ◽  
Regional Differences ◽  
Grey Matter ◽  
Anterior Cerebral Artery ◽  
Cerebral Arteries ◽  
Brain Regions ◽  
Aging Brain ◽  
Age Related ◽  
Vessel Width

Vessel loss in the aging brain is commonly reported, yet important questions remain concerning whether there are regional vulnerabilities and what mechanisms could account for these regional differences, if they exist. Here we imaged and quantified vessel length, tortuosity and width in 15 brain regions in young adult and aged mice. Our data indicate that vessel loss was most pronounced in white matter followed by cortical, then subcortical grey matter regions, while some regions (visual cortex, amygdala, thalamus) showed no decline with aging. Regions supplied by the anterior cerebral artery were more vulnerable to loss than those supplied by middle or posterior cerebral arteries. Vessel width and tortuosity generally increased with age but neither reliably predicted regional vessel loss. Since capillaries are naturally prone to plugging and prolonged obstructions often lead to vessel pruning, we hypothesized that regional susceptibilities to plugging could help predict vessel loss. By mapping the distribution of microsphere-induced capillary obstructions, we discovered that regions with a higher density of persistent obstructions were more likely to show vessel loss with aging and vice versa. These findings indicate that age-related vessel loss is region specific and can be explained, at least partially, by regional susceptibilities to capillary plugging.

scholarly journals Late chronotype is linked to greater cortical thickness in the left fusiform and entorhinal gyri

2021 ◽  
Author(s):  
Michal Rafal Zareba ◽  
Magdalena Fafrowicz ◽  
Tadeusz Marek ◽  
Ewa Beldzik ◽  
Halszka Oginska ◽  
...  
Keyword(s):  
White Matter ◽  
Cortical Thickness ◽  
Grey Matter ◽  
Brain Regions ◽  
Affective Processing ◽  
Grey Matter Volume ◽  
Imaging Data ◽  
White Matter Volume ◽  
Matter Volume ◽  
Anatomical Basis

Abstract Humans can be classified as early, intermediate and late chronotypes based on the preferred sleep and wakefulness patterns. The anatomical basis of these distinctions remains largely unexplored. Using magnetic resonance imaging data from 113 healthy young adults (71 females), we aimed to replicate cortical thickness and grey matter volume chronotype differences reported earlier in the literature using a greater sample size, as well as to explore the volumetric white matter variation linked to contrasting circadian phenotypes. Instead of comparing the chronotypes, we correlated the individual chronotype scores with their morphometric brain measures. The results revealed one cluster in the left fusiform and entorhinal gyri showing increased cortical thickness with increasing preference for eveningness, potentially providing an anatomical substrate for chronotype-sensitive affective processing. No significant results were found for grey and white matter volume. We failed to replicate cortical thickness and volumetric grey matter distinctions in the brain regions reported in the literature. Furthermore, we found no association between white matter volume and chronotype. Thus, while this study confirms that circadian preference is associated with specific structural substrates, it adds to the growing concerns that reliable and replicable neuroimaging research requires datasets much larger than those commonly used.

scholarly journals Functionnectome as a framework to analyse the contribution of brain circuits to fMRI

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Victor Nozais ◽  
Stephanie J. Forkel ◽  
Chris Foulon ◽  
Laurent Petit ◽  
Michel Thiebaut de Schotten
Keyword(s):  
White Matter ◽  
Grey Matter ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Functional Networks ◽  
Language Functions ◽  
Brain Circuits ◽  
Joint Contribution ◽  
The Relationship ◽  
The Brain

AbstractIn recent years, the field of functional neuroimaging has moved away from a pure localisationist approach of isolated functional brain regions to a more integrated view of these regions within functional networks. However, the methods used to investigate functional networks rely on local signals in grey matter and are limited in identifying anatomical circuitries supporting the interaction between brain regions. Mapping the brain circuits mediating the functional signal between brain regions would propel our understanding of the brain’s functional signatures and dysfunctions. We developed a method to unravel the relationship between brain circuits and functions: The Functionnectome. The Functionnectome combines the functional signal from fMRI with white matter circuits’ anatomy to unlock and chart the first maps of functional white matter. To showcase this method’s versatility, we provide the first functional white matter maps revealing the joint contribution of connected areas to motor, working memory, and language functions. The Functionnectome comes with an open-source companion software and opens new avenues into studying functional networks by applying the method to already existing datasets and beyond task fMRI.

scholarly journals Brain structural correlates of autistic traits across the diagnostic divide: A grey matter and white matter microstructure study

2021 ◽  
Author(s):  
Varun Arunachalam Chandran ◽  
Christos Pliatsikas ◽  
Janina Neufeld ◽  
Garret O'Connell ◽  
Anthony Haffey ◽  
...  
Keyword(s):  
White Matter ◽  
Grey Matter ◽  
Diffusion Tensor ◽  
Autistic Traits ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Brain Volumes ◽  
White Matter Microstructure ◽  
The Social ◽  
Brain Correlates

Autism Spectrum Disorders (ASD) are a set of neurodevelopmental conditions characterised by difficulties in social interaction and communication as well as stereotyped and restricted patterns of interest. Autistic traits exist in a continuum across the general population, whilst the extreme end of this distribution is diagnosed as clinical ASD. While many studies have investigated brain structure in autism using a case-control design, few have used a dimensional approach. To add to this growing body of literature, we investigated the structural brain correlates of autistic traits in a mixed sample of adults (N=91) with and without a clinical diagnosis of autism. We examined regional brain volumes (using voxel-based morphometry and surface-based morphometry) and white matter microstructure properties (using Diffusion Tensor Imaging). Our findings show widespread grey matter differences, including in the social brain regions, and some evidence for white matter microstructure differences related to higher autistic traits. These grey matter and white matter microstructure findings from our study are consistent with previous reports and support the brain structural differences in ASD. These findings provide further support for shared aetiology for autistic traits across the diagnostic divide.

Individual differences in white and grey matter structure associated with verbal habits of thought

2019 ◽  
Author(s):  
Justin C. Hayes ◽  
Katherine L Alfred ◽  
Rachel Pizzie ◽  
Joshua S. Cetron ◽  
David J. M. Kraemer
Keyword(s):  
Individual Differences ◽  
White Matter ◽  
Cognitive Processing ◽  
Grey Matter ◽  
Structural Changes ◽  
Structural Integrity ◽  
Diffusion Tensor ◽  
Self Report ◽  
White Matter Tracts

Modality specific encoding habits account for a significant portion of individual differences reflected in functional activation during cognitive processing. Yet, little is known about how these habits of thought influence long-term structural changes in the brain. Traditionally, habits of thought have been assessed using self-report questionnaires such as the visualizer-verbalizer questionnaire. Here, rather than relying on subjective reports, we measured habits of thought using a novel behavioral task assessing attentional biases toward picture and word stimuli. Hypothesizing that verbal habits of thought are reflected in the structural integrity of white matter tracts and cortical regions of interest, we used diffusion tensor imaging and volumetric analyses to assess this prediction. Using a whole-brain approach, we show that word bias is associated with increased volume in several bilateral language regions, in both white and grey matter parcels. Additionally, connectivity within white matter tracts within an a priori speech production network increased as a function of word bias. These results demonstrate long-term structural and morphological differences associated with verbal habits of thought.

scholarly journals Modelling the cascade of biomarker changes in GRN-related frontotemporal dementia

2021 ◽  
pp. jnnp-2020-323541
Author(s):  
Jessica L Panman ◽  
Vikram Venkatraghavan ◽  
Emma L van der Ende ◽  
Rebecca M E Steketee ◽  
Lize C Jiskoot ◽  
...  
Keyword(s):  
White Matter ◽  
Frontotemporal Dementia ◽  
Disease Severity ◽  
Grey Matter ◽  
Clinical Stage ◽  
Data Driven ◽  
Grey Matter Volume ◽  
Matter Volume ◽  
Mutation Carriers ◽  
Individual Disease

ObjectiveProgranulin-related frontotemporal dementia (FTD-GRN) is a fast progressive disease. Modelling the cascade of multimodal biomarker changes aids in understanding the aetiology of this disease and enables monitoring of individual mutation carriers. In this cross-sectional study, we estimated the temporal cascade of biomarker changes for FTD-GRN, in a data-driven way.MethodsWe included 56 presymptomatic and 35 symptomatic GRN mutation carriers, and 35 healthy non-carriers. Selected biomarkers were neurofilament light chain (NfL), grey matter volume, white matter microstructure and cognitive domains. We used discriminative event-based modelling to infer the cascade of biomarker changes in FTD-GRN and estimated individual disease severity through cross-validation. We derived the biomarker cascades in non-fluent variant primary progressive aphasia (nfvPPA) and behavioural variant FTD (bvFTD) to understand the differences between these phenotypes.ResultsLanguage functioning and NfL were the earliest abnormal biomarkers in FTD-GRN. White matter tracts were affected before grey matter volume, and the left hemisphere degenerated before the right. Based on individual disease severities, presymptomatic carriers could be delineated from symptomatic carriers with a sensitivity of 100% and specificity of 96.1%. The estimated disease severity strongly correlated with functional severity in nfvPPA, but not in bvFTD. In addition, the biomarker cascade in bvFTD showed more uncertainty than nfvPPA.ConclusionDegeneration of axons and language deficits are indicated to be the earliest biomarkers in FTD-GRN, with bvFTD being more heterogeneous in disease progression than nfvPPA. Our data-driven model could help identify presymptomatic GRN mutation carriers at risk of conversion to the clinical stage.

scholarly journals Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arian Ashourvan ◽  
Preya Shah ◽  
Adam Pines ◽  
Shi Gu ◽  
Christopher W. Lynn ◽  
...  
Keyword(s):  
White Matter ◽  
Maximum Entropy ◽  
Large Scale ◽  
Structural Connectivity ◽  
Quantitative Relationship ◽  
Brain Regions ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Activation Patterns ◽  
Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

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