Biology subjects

Zeighami, Y.

Publications and source records attributed to Zeighami, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Transcriptional and neurochemical signatures of cerebral blood flow alterations in schizophrenia and the clinical high-risk state for psychosis

The brain integrates multiple scales of description, from the level of cells and molecules to large-scale networks and behaviour, and understanding the relationships between these layers may be fundamental to advancing our understanding of how the brain works in health and disease. Recent neuroimaging research has shown that alterations in brain function that are associated with schizophrenia spectrum disorders (SSD) are already present in young adults at clinical high-risk for psychosis (CHR-P), yet the cellular and molecular determinants of these alterations are not well understood. Here, combining regional cerebral blood flow (rCBF) data with existing transcriptomic and neurotransmitter data, we show that cell-types involved in stress response and inflammation, as well as the dopamine, acetylcholine, GABAA and NMDA receptor systems, align as shared and distinct cellular and neurochemical signatures of rCBF phenotypes in people with SSD and those at CHR-P. Decoding the biological pathways involved in neuroimaging-based psychosis phenotypes may provide a basis for the development of novel interventions.

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bioRxiv2024

The Douglas Bell Canada Brain Bank Post-mortem Brain Imaging Protocol

Magnetic resonance imaging (MRI) is a valuable non-invasive tool that has been widely used for in vivo investigations of brain morphometry and microstructural characteristics. Postmortem MRIs can provide complementary anatomical and microstructural information to in vivo imaging and ex vivo neuropathological assessments without compromising the sample for future investigations. We have developed a postmortem MRI protocol for the brain specimens of the Douglas-Bell Canada Brain Bank (DBCBB), the largest brain bank in Canada housing over 3000 neurotypical and diseased brain specimens, that allows for acquisition of high-resolution 3T and 7T MRIs. Our protocol can be used to scan DBCBB specimens with minimal tissue manipulation, allowing for feasibly scanning large numbers of postmortem specimens while retaining the quality of the tissue for downstream histology and immunohistochemistry assessments. We demonstrate the robustness of this protocol in spite of the dependency of image quality on fixation by acquiring data on the first day of extraction and fixation, to over twenty years post fixation. The acquired images can be used to perform volumetric segmentations, cortical thickness measurements, and quantitative analyses which can be potentially used to link MRI-derived and ex vivo histological measures, assaying both the normative organization of the brain and ex vivo measures of pathology.

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bioRxiv2023

Investigating the impact of motion in the scanner on brain age predictions

IntroductionBrain Age Gap (BAG) is defined as the difference between the brains predicted age and the chronological age of an individual. Magnetic resonance imaging (MRI)-based BAG can quantify acceleration of brain aging, and is used to infer brain health as aging and disease interact. Motion in the scanner is a common occurrence that can affect the acquired MRI data and act as a major confound in the derived models. As such, age-related changes in head motion may impact the observed age-related differences. However, the relationship between head motion and BAG as estimated by structural MRI has not been systematically examined. The aim of this study is to assess the impact of motion on voxel-based morphometry (VBM) based BAG. MethodsData were obtained from two sources: i) T1-weighted (T1w) MRIs from the Cambridge Centre for Ageing and Neuroscience (CamCAN) were used to train the brain age prediction model, and ii) T1w MRIs from the Movement-related artifacts (MR-ART) dataset were used to assess the impact of motion on BAG. MR-ART includes one motion-free and two motion-affected (one low and one high) 3D T1w MRIs. We also visually rated the motion levels of the MR-ART MRIs from 0 to 5, with 0 meaning no motion and 5 high motion levels. All images were pre-processed through a standard VBM pipeline. GM density across cortical and subcortical regions were then used to train the brain age prediction model and assess the relationship between BAG and MRI motion. Principal component analysis was used to perform dimension reduction and extract the VBM-based features. BAG was estimated by regressing out the portion of delta age explained by chronological age. Linear mixed effects models were used to investigate the relationship between BAG and motion session as well as motion severity, including participant IDs as random effects. We repeated the same analysis using cortical thickness based on FreeSurfer 7.4.1 and to compare the results for volumetric versus surface-based measures of brain morphometry. ResultsIn contrast with the session with no induced motion, predicted delta age was significantly higher for high motion sessions 2.35 years (t = 5.17, p < 0.0001), with marginal effect for low motion sessions 0.95 years (t = 2.11, p=0.035) for VBM analysis as well as 3.46 years (t = 11.45, p < 0.0001) for high motion and 2.28 years (t = 7.54, p<0.0001) for low motion based on cortical thickness. In addition, delta age was significantly associated with motion severity as evaluated by visual rating 0.45 years per rating level (t = 4.59, p < 0.0001) for VBM analysis and 0.83 years per motion level (t = 12.89, p<0.0001) for cortical thickness analysis. ConclusionMotion in the scanner can significantly impact brain age estimates, and needs to be accounted for as a confound, particularly when studying populations that are known to have higher levels of motion in the scanner. These results have significant implications for brain age studies in aging and neurodegeneration. Based on these findings, we recommend assessment and inclusion of visual motion ratings in such studies. In cases that the visual rating proves prohibitive, we recommend the inclusion of normalized Euler number from FreeSurfer as defined in the manuscript as a covariate in the models.

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bioRxiv2023

Distinctive Whole-brain Cell-Types Strongly Predict Tissue Damage Patterns in Eleven Neurodegenerative Disorders

For over a century, brain research narrative has mainly centered on neuron cells. Accordingly, most neurodegenerative studies focus on neuronal dysfunction and their selective vulnerability, while we lack comprehensive analyses of other major cell types contribution. By unifying spatial gene expression, structural MRI, and cell deconvolution, here we describe how the human brain distribution of canonical cell types extensively predicts tissue damage in thirteen neurodegenerative conditions, including early-and late-onset Alzheimers disease, Parkinsons disease, dementia with Lewy bodies, amyotrophic lateral sclerosis, mutations in presenilin-1, and three clinical variants of frontotemporal lobar degeneration (behavioural variant, semantic and non-fluent primary progressive aphasia) along with associated 3-repeat and 4-repeat tauopathies and TDP43 proteinopathies types A and C. We reconstructed comprehensive whole-brain reference maps of cellular abundance for six major cell types and identified characteristic axes of spatial overlapping with atrophy. Our results support the strong mediating role of non-neuronal cells, primarily microglia and astrocytes, in spatial vulnerability to tissue loss in neurodegeneration, with distinct and shared across-disorders pathomechanisms. These observations provide critical insights into the multicellular pathophysiology underlying spatiotemporal advance in neurodegeneration. Notably, they also emphasize the need to exceed the current neuro-centric view of brain diseases, supporting the imperative for cell-specific therapeutic targets in neurodegeneration.

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bioRxiv2021

Structural and cellular transcriptome foundations of human brain disease

Genes associated with risk for brain disease exhibit characteristic expression patterns that reflect both anatomical and cell type relationships. Brain-wide transcriptomic patterns of disease risk genes provide a molecular based signature for identifying disease association, often differing from common phenotypic classification. Analysis of adult brain-wide transcriptomic patterns associated with 40 human brain diseases identified five major transcriptional patterns, represented by tumor-related, neurodegenerative, psychiatric and substance abuse, and two mixed groups of diseases. Brain disease risk genes exhibit unique anatomic transcriptomic signatures, based on differential co-expression, that often uniquely identify the disease. For cortical expressing diseases, single nucleus data in the middle temporal gyrus reveals cell type expression gradients separating neurodegenerative, psychiatric, and substance abuse diseases. By homology mapping of cell types across mouse and human, transcriptomic disease signatures are found largely conserved, but with psychiatric and substance abuse related diseases showing important specific species differences. These results describe the structural and cellular transcriptomic landscape of disease in the adult brain, highlighting significant homology with the mouse yet indicating where human data is needed to further refine our understanding of disease-associated genes.

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bioRxiv2021

Spontaneous Neural Activity Changes after Bariatric Surgery: a resting-state fMRI study

1.BackgroundMetabolic disorders associated with obesity could lead to alterations in brain structure and function. Whether these changes can be reversed after weight loss is unclear. Bariatric surgery provides a unique opportunity to address these questions because it induces marked weight loss and metabolic improvements which in turn may impact the brain in a longitudinal fashion. Previous studies found widespread changes in grey matter (GM) and white matter (WM) after bariatric surgery. However, findings regarding changes in spontaneous neural activity following surgery, as assessed with the fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity of neural activity (ReHo), are scarce and heterogenous. In this study, we used a longitudinal design to examine the changes in spontaneous neural activity after bariatric surgery (comparing pre- to post-surgery), and to determine whether these changes are related to cardiometabolic variables. MethodsThe study included 57 participants with severe obesity (mean BMI=43.1{+/-}4.3kg/m2) who underwent sleeve gastrectomy (SG), biliopancreatic diversion with duodenal switch (BPD), or Roux-en-Y gastric bypass (RYGB), scanned prior to bariatric surgery and at follow-up visits of 4 months (N=36), 12 months (N=29), and 24 months (N=14) after surgery. We examined fALFF and ReHo measures across 1022 cortical and subcortical regions (based on combined Schaeffer-Xiao parcellations) using a linear mixed effect model. Voxel-based morphometry (VBM) based on T1-weighted images was also used to measure GM density in the same regions. We also used an independent sample from the Human Connectome Project (HCP) to assess regional differences between individuals who had normal-weight (N=46) or severe obesity (N=46). ResultsWe found a global increase in the fALFF signal with greater increase within dorsolateral prefrontal cortex, precuneus, inferior temporal gyrus, and visual cortex. This effect was more significant 4 months after surgery. The increase within dorsolateral prefrontal cortex, temporal gyrus, and visual cortex was more limited after 12 months and only present in the visual cortex after 24 months. These increases in neural activity measured by fALFF were also significantly associated with the increase in GM density following surgery. Furthermore, the increase in neural activity was significantly related to post-surgery weight loss and improvement in cardiometabolic variables, such as insulin resistance index and blood pressure. In the independent HCP sample, normal-weight participants had higher global and regional fALFF signals, mainly in dorsolateral/medial frontal cortex, precuneus and middle/inferior temporal gyrus compared to the obese participants. These BMI-related differences in fALFF were associated with the increase in fALFF 4 months post-surgery especially in regions involved in control, default mode and dorsal attention networks. ConclusionsBariatric surgery-induced weight loss and improvement in metabolic factors are associated with widespread global and regional increases in neural activity, as measured by fALFF signal. These findings alongside the higher fALFF signal in normal-weight participants compared to participants with severe obesity in an independent dataset suggest an early recovery in the neural activity signal level after the surgery.

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bioRxiv2020

Association versus Prediction: the impact of cortical surface smoothing and parcellation on brain age

Association and prediction studies of the brain target the biological consequences of aging and their impact on brain function. Such studies are conducted using different smoothing levels and parcellations at the preprocessing stage, on which their results are dependent. However, the impact of these parameters on the relationship between association values and prediction accuracy is not established. In this study, we used cortical thickness and its relationship with age to investigate how different smoothing and parcellation levels affect the detection of age-related brain correlates as well as brain age prediction accuracy. Our main measures were resel numbers - resolution elements - and age-related variance explained. Using these common measures enabled us to directly compare parcellation and smoothing effects in both association and prediction studies. In our sample of N=608 participants with age range 18-88, we evaluated age-related cortical thickness changes as well as brain age prediction. We found a negative relationship between prediction performance and correlation values for both parameters. Our results also quantify the relationship between delta age estimates obtained based on different processing parameters. Furthermore, with the direct comparison of the two approaches, we highlight the importance of correct choice of smoothing and parcellation parameters in each task, and how they can affect the results of the analysis in opposite directions.

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bioRxiv2020

PhyloBrain atlas: a cortical brain MRI atlas following a phylogenetic approach

Cortical atlases constitute a consistent division of the human cortex into areas that have common structural as well as meaningful and distinctive functional characteristics. The most widely used atlases follow the cytoarchitectonic and myeloarchitectonic characteristics of the cortex and have been combined to the standard anatomical nomenclature of gyri and sulci. More recently, common functional features depicted by resting state functional MRI have also guided the division of the cortical brain in functional regions of interest. However, to date, there are no atlases that divide the cortex considering the common evolutionary changes experienced by the mammalian cortex. Hence, the present study proposes the division of cortical areas into five main regions of interest (ROIs) following a phylogenetic approach: 1- archicortex, 2- paleocortex, 3- peri-archicortex, 4- proisocortex, 5-neocortex, and twelve neocortical sub-ROIs: 5.1.temporopolar, 5.2.post-central, 5.3.pre-central, 5.4.pericalcarine, 5.5.superior temporal, 5.6.middle temporal, 5.7.precuneus, 5.8.insular, 5.9.inferior parietal, 5.10.caudal anterior, 5.11.posterior cingulate, and 5.12.lingual gyrus. The segmentations were done using the T1-weighted MNI-ICBM152 non-linear 6th generation symmetric average brain MRI model.

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bioRxiv2019

Neural correlates of evidence and urgency during human perceptual decision-making in dynamically changing conditions

Current models of decision-making assume that the brain gradually accumulates evidence and drifts towards a threshold which, once crossed, results in a choice selection. These models have been especially successful in primate research, however transposing them to human fMRI paradigms has proved challenging. Here, we exploit the face-selective visual system and test whether decoded emotional facial features from multivariate fMRI signals during a dynamic perceptual decision-making task are related to the parameters of computational models of decision-making. We show that trial-by-trial variations in the pattern of neural activity in the fusiform gyrus reflect facial emotional information and modulate drift rates during deliberation. We also observed an inverse-urgency signal based in the caudate nucleus that was independent of sensory information but appeared to slow decisions, particularly when information in the task was ambiguous. Taken together, our results characterize how decision parameters from a computational model (i.e., drift rate and urgency signal) are involved in perceptual decision-making and reflected in the activity of the human brain.

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bioRxiv2019

Neuroanatomical Changes in White and Grey Matter after Sleeve Gastrectomy

BackgroundMRI studies show that obese adults have reduced grey (GM) and white matter (WM) tissue density as well as altered WM integrity. It remains to be examined if bariatric surgery induces structural brain changes. The aim of this study is to characterize GM and WM density changes in a longitudinal setting, comparing pre- and post-operation and to determine whether these changes are related to inflammation and cardiometabolic markers.\n\nMethods29 severely obese participants (age: 45.9{+/-}7.8 years) scheduled to undergo sleeve gastrectomy (SG) were recruited. High-resolution T1-weighted anatomical images were acquired 1 month prior to as well as 4 and 12 months after surgery. GM and WM densities were quantified using voxel-based morphometry (VBM). Circulating lipid profile, glucose, insulin and inflammatory markers (interleukin (IL)-6, C-reactive protein (CRP) and lipopolysaccharide-binding protein (LBP) were measured at each time point. A linear mixed effect model was used to compare brain changes before and after SG, controlling for age, gender, initial BMI and diabetic status. To assess the associations between changes in adiposity, metabolism and inflammation and changes in GM or WM density, the mean GM and WM densities were extracted across all the participants using atlas, and linear mixed-effect models were used.\n\nResultsAs expected, weight, BMI, waist circumference and neck circumference significantly decreased after SG compared with baseline (p<0.001 for all). A widespread increase in WM density was observed after surgery, particularly in the cerebellum, brain stem, cerebellar peduncle, cingulum, corpus callosum and corona radiata (p<0.05, after FDR correction). Significant increases in GM density were observed 4 months after SG compared to baseline in several brain regions such as the bilateral occipital cortex, temporal cortex, precentral gyrus and cerebellum as well as right fusiform gyrus, right hippocampus and right insula. These GM and WM increases were more pronounced and widespread after 12 months and were significantly associated with post-operative weight loss and the improvement of metabolic alterations. Our linear mixed-effect models also showed strong associations between post-operative reductions in LBP, a marker of inflammation, and increased WM density. To confirm our results, we tested whether the peak of each significant region showed BMI-related differences in an independent dataset (Human Connectome Project). We matched a group of severely obese individuals with a group of lean individuals for age, gender and ethnicity. Severe obesity was associated with reduced WM density in the brain stem and cerebellar peduncle as well as reduced GM density in cerebellum, regions that significantly changed after surgery (p<0.01 for all clusters).\n\nConclusionsBariatric surgery-induced weight loss and improvement in metabolic alterations is associated with widespread increases in WM and GM densities. These post-operative changes overlapped with baseline brain differences between severely obese and normal-weight individuals, which may suggest a recovery of WM and GM alterations after bariatric surgery.

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bioRxiv2019

An MRI Atrophy Biomarker Predicts Global Prognosis in Early De Novo Parkinson's Disease

BackgroundCommonly used neuroimaging biomarkers in Parkinsons disease (PD) are useful for diagnosis but poor at predicting outcomes. We explored whether an atrophy pattern from whole-brain structural MRI, measured in the drug-naive early stage, could predict PD prognosis. Methods362 de novo PD patients with T1-weighted MRI (n=222 for the main analysis, 140 for the validation analysis) were recruited from the Parkinsons Progression Markers Initiative (PPMI). We investigated a previously identified PD-specific network atrophy pattern as a potential biomarker of disease severity and prognosis. Progression trajectories of motor function (MDS-UPDRS-part III), cognition (Montreal Cognitive Assessment (MoCA)), and a global composite outcome measure were compared between atrophy tertiles using mixed effect models. The prognostic value of the MRI atrophy measure was compared with 123I ioflupane single photon emission computed tomography, the postural-instability-gait-disturbance score, and cerebrospinal fluid markers. FindingsAfter 4.5 years follow-up, PD-specific atrophy network score at baseline significantly predicted change in UPDRS-part III (r=-0.197, p=0.003), MoCA (r=0.253, p=0.0002) and global composite outcome (r=-0.249, p=0.0002). Compared with the 3rd tertile (i.e. least atrophy), the tertile with the highest baseline atrophy (i.e. the 1st tertile) had a 3-point annual faster progression in UPDRS-part III (p=0.012), faster worsening of posture-instability gait scores (+0.21 further annual increase, p<0.0001), faster decline in MoCA (-0.74 further annual decline in MoCA, p=0.0372) and a +0.38 (p=0.0029) faster annual increase in the global composite z-score. All findings were replicated in a validation analysis using 1.5T MRI. By comparison, the other biomarkers were limited in their ability to predict prognosis either in the main or validation analysis. InterpretationA PD-specific network atrophy pattern predicts progression of motor, cognitive, and global outcome in PD, and is a stronger predictor of prognosis than any of the other tested biomarkers. Therefore, it has considerable potential as a prognostic biomarker for clinical trials of early PD.

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bioRxiv2018

Heterogeneity of Incipient Atrophy Patterns in Parkinsons Disease

Parkinsons Disease (PD) is a the second most common neurodegenerative disorder after Alzheimers disease and is characterized by cell death in the amygdala and in substructures of the basal ganglia such as the substantia nigra. Since neuronal loss in PD leads to measurable atrophy patterns in the brain, there is clinical value in understanding where exactly the pathology emerges in each patient and how incipient atrophy relates to the future spread of disease. A recent seed-inference algorithm combining an established network-diffusion model with an L1-penalized optimization routine led to new insights regarding the non-stereotypical origins of Alzheimers pathologies across individual subjects. Here, we leverage the same technique to PD patients, demonstrating that the high variability in their atrophy patterns also translates into heterogeneous seed locations. Our individualized seeds are significantly more predictive of future atrophy than a single seed placed at the substantia nigra or the amygdala. We also found a clear distinction in seeding patterns between two PD subgroups - one characterized by predominant involvement of brainstem and ventral nuclei, and the other by more widespread frontal and striatal cortices. This might be indicative of two distinct etiological mechanisms operative in PD. Ultimately, our methods demonstrate that the early stages of the disease may exhibit incipient atrophy patterns that are more complex and variable than generally appreciated.

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bioRxiv2018

Connectome architecture, gene expression and functional co-activation shape the propagation of misfolded proteins in neurodegenerative disease

It is becoming increasingly clear that brain network organization shapes the course and expression of neurodegenerative diseases. Parkinsons disease (PD) is marked by progressive spread of atrophy from the midbrain to subcortical structures and eventually, to the cerebral cortex. Recent discoveries suggest that the neurodegenerative process involves the misfolding and prion-like propagation of endogenous -synuclein via axonal projections. However, the mechanisms that translate local \"synucleinopathy\" to large-scale network dysfunction and atrophy remain unknown. Here we use an agent-based epidemic spreading model to integrate structural connectivity, functional connectivity and gene expression, and to predict sequential volume loss due to neurodegeneration. The dynamic model replicates the spatial and temporal patterning of empirical atrophy in PD and implicates the substantia nigra as the disease epicenter. We reveal a significant role for both connectome topology and geometry in shaping the distribution of atrophy. The model also demonstrates that SNCA and GBA transcription influence -synuclein concentration and local regional vulnerability. Functional co-activation further amplifies the course set by connectome architecture and gene expression. Altogether, these results support the theory that the progression of PD is a multifactorial process that depends on both cell-to-cell spreading of misfolded proteins and regional vulnerability.

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bioRxiv2018

Neural and behavioral endophenotypes of obesity

BackgroundImpulsivity increases the risk for obesity and weight gain. However, the precise role of impulsivity in the aetiology of overeating behavior and obesity is currently unknown. Here we examined the relationships between personality-related measures of impulsivity, Uncontrolled Eating, BMI, and longitudinal weight changes. Additionally, we analyzed the associations between general impulsivity domains and brain cortical thickness to elucidate brain vulnerability factors related to weight gain. MethodsStudents in their first year of university - a risky period for weight gain - completed questionnaire measures of impulsivity and eating behavior at the beginning (N = 2318) of the school year. We also collected their weight at the end of the term (N = 1197). Impulsivity was divided into factors stress reactivity, reward sensitivity and lack of self-control. Using structural equation models, we tested the plausibility of a hierarchical relationship, in which impulsivity traits were associated with Uncontrolled Eating, which in turn predicted BMI and weight change. 71 participants underwent T1-weighted MRI to investigate the correlation between impulsivity and cortical thickness. ResultsImpulsivity traits showed positive correlations with Uncontrolled Eating. Higher scores in Uncontrolled Eating were in turn associated with higher BMI. None of the impulsivity-related measurements nor Uncontrolled Eating were correlated with longitudinal weight gain. Higher stress sensitivity was associated with increased cortical thickness in the superior temporal gyrus. Lack of self-control was positively associated with increased thickness in the superior medial frontal gyrus. Finally, higher reward sensitivity was associated with lower thickness in the inferior frontal gyrus. ConclusionThe present study provides a comprehensive characterization of the relationships between different facets of impulsivity and obesity. We show that differences in impulsivity domains might be associated with BMI via Uncontrolled Eating. Our results might inform future clinical strategies aimed at fostering self-control abilities to prevent and/or treat unhealthy weight gain.

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bioRxiv2018

Dopamine signaling modulates the stability and integration of intrinsic brain networks

Dopaminergic projections are hypothesized to stabilize neural signaling and neural representations, but how they shape regional information processing and large-scale network interactions remains unclear. Here we investigated effects of lowered dopamine levels on within-region temporal signal variability (measured by sample entropy) and between-region functional connectivity (measured by pairwise temporal correlations) in the healthy brain at rest. The acute phenylalanine and tyrosine depletion (APTD) method was used to decrease dopamine synthesis in 51 healthy participants who underwent resting-state functional MRI (fMRI) scanning. Functional connectivity and regional signal variability were estimated for each participant. Multivariate partial least squares (PLS) analysis was used to statistically assess changes in signal variability following APTD as compared to the balanced control treatment. The analysis captured a pattern of increased regional signal variability following dopamine depletion. Changes in hemodynamic signal variability were concomitant with changes in functional connectivity, such that nodes with greatest increase in signal variability following dopamine depletion also experienced greatest decrease in functional connectivity. Our results suggest that dopamine may act to stabilize neural signaling, particularly in networks related to motor function and orienting attention towards behaviorally-relevant stimuli. Moreover, dopaminedependent signal variability is critically associated with functional embedding of individual areas in large-scale networks.

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bioRxiv2017

Central Role of Cognitive Control Networks in Weight Loss During Voluntary Calorie Restriction

Insufficient responses to hypocaloric diets have been attributed to hormonal adaptations that override self-control of food intake. We tested this hypothesis by measuring brain fMRI reactivity to food cues and circulating energy-balance hormones in 24 overweight/obese participants before, and 1 and 3 months after starting a calorie restriction diet. Increased activity in prefrontal regions at month 1 correlated with weight loss at months 1 and 3. Weight loss was also correlated with increased plasma ghrelin and decreased leptin at month 1, and these changes were associated with greater food cue reactivity in reward-related brain regions. However, the reduction in leptin did not counteract weight loss; indeed, it was correlated with further weight loss at month 3. Activation in a network of prefrontal regions associated with self-control could contribute to individual differences in weight loss and maintenance, whereas we failed to find that the hormonal adaptations play a major role.

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bioRxiv2017

White Matter Hyperintensities and Cognitive Decline in de Novo Parkinson’s Disease Patients

ObjectiveWhite Matter Hyperintensities (WMHs) are associated with cognitive decline in normative aging and Alzheimers disease. However, the pathogenesis of cognitive decline in Parkinsons disease (PD) is not directly related to vascular causes, and therefore the role of WMHs in PD remains unclear. If WMH has a higher impact on cognitive decline in PD, vascular pathology should be assessed and treated with a higher priority in this population. Here we investigate whether WMH leads to increased cognitive decline in PD, and if these effects relate to cortical thinning\n\nMethodsTo investigate the role of WMHs in PD, it is essential to study recently-diagnosed/non-treated patients. De novo PD patients and age-matched controls (NPD=365,NControl=174) with FLAIR/T2-weighted scans at baseline were selected from Parkinsons Progression Markers Initiative (PPMI). WMHs and cortical thickness were measured to analyse the relationship between baseline WMHs and future cognitive decline (follow-up:4.09{+/-}1.14 years) and cortical thinning (follow-up:1.05{+/-}0.10 years).\n\nResultsHigh WMH load (WMHL) at baseline in PD was associated with increased cognitive decline, significantly more than i) PDs with low WMHL and ii) controls with high WMHL. Furthermore, PD patients with higher baseline WMHL showed more cortical thinning in right frontal lobe than subjects with low WMHL. Cortical thinning of this region also predicted decline in performance on a cognitive test.\n\nInterpretationPresence of WMHs in de novo PD patients predicts greater future cognitive decline and cortical thinning than in normal aging. Recognizing WMHs as a potential predictor of cognitive deficit in PD provides an opportunity for timely interventions.

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bioRxiv2017

Neurobehavioural Correlates of Obesity are Largely Heritable

Recent molecular genetic studies have shown that the majority of genes associated with obesity are expressed in the central nervous system. Obesity has also been associated with neurobehavioural factors such as brain morphology, cognitive performance, and personality. Here, we tested whether these neurobehavioural factors were associated with the heritable variance in obesity measured by body mass index (BMI) in the Human Connectome Project (N=895 siblings). Phenotypically, cortical thickness findings supported the \"right brain hypothesis\" for obesity. Namely, increased BMI associated with decreased cortical thickness in right frontal lobe and increased thickness in the left frontal lobe, notably in lateral prefrontal cortex. In addition, lower thickness and volume in entorhinal-parahippocampal structures, and increased thickness in parietal-occipital structures in obese participants supported the role of visuospatial function in obesity. Brain morphometry results were supported by cognitive tests, which outlined obesitys negative association with visuospatial function, verbal episodic memory, impulsivity, and cognitive flexibility. Personality-obesity correlations were inconsistent. We then aggregated the effects for each neurobehavioural factor for a behavioural genetics analysis and demonstrated the factors genetic overlap with obesity. Namely, cognitive test scores and brain morphometry had 0.25 - 0.45 genetic correlations with obesity, and the phenotypic correlations with obesity were 77-89% explained by genetic factors. Neurobehavioural factors also had some genetic overlap with each other. In summary, obesity has considerable genetic overlap with brain and cognitive measures. This supports the theory that obesity is inherited via brain function, and may inform intervention strategies.\n\nSignificance StatementObesity is a widespread heritable health condition. Evidence from psychology, cognitive neuroscience, and genetics has proposed links between obesity and the brain. The current study tested whether the heritable variance in obesity is explained by brain and behavioural factors in a large brain imaging cohort that included multiple related individuals. We found that the heritable variance in obesity had genetic correlations 0.25 - 0.45 with cognitive tests, cortical thickness, and regional brain volume. In particular, obesity was associated with frontal lobe asymmetry and differences in temporal-parietal perceptual systems. Further, we found genetic overlap between certain brain and behavioural factors. In summary, the genetic vulnerability to obesity is expressed in the brain. This may inform intervention strategies.

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