Human Connectome Project Multimodal Parcellation Research Articles

Selective activations and functional connectivities to the sight of faces, scenes, body parts and tools in visual and non-visual cortical regions leading to the human hippocampus

Connectivity maps are now available for the 360 cortical regions in the Human Connectome Project Multimodal Parcellation atlas. Here we add function to these maps by measuring selective fMRI activations and functional connectivity increases to stationary visual stimuli of faces, scenes, body parts and tools from 956 HCP participants. Faces activate regions in the ventrolateral visual cortical stream (FFC), in the superior temporal sulcus (STS) visual stream for face and head motion; and inferior parietal visual (PGi) and somatosensory (PF) regions. Scenes activate ventromedial visual stream VMV and PHA regions in the parahippocampal scene area; medial (7m) and lateral parietal (PGp) regions; and the reward-related medial orbitofrontal cortex. Body parts activate the inferior temporal cortex object regions (TE1p, TE2p); but also visual motion regions (MT, MST, FST); and the inferior parietal visual (PGi, PGs) and somatosensory (PF) regions; and the unpleasant-related lateral orbitofrontal cortex. Tools activate an intermediate ventral stream area (VMV3, VVC, PHA3); visual motion regions (FST); somatosensory (1, 2); and auditory (A4, A5) cortical regions. The findings add function to cortical connectivity maps; and show how stationary visual stimuli activate other cortical regions related to their associations, including visual motion, somatosensory, auditory, semantic, and orbitofrontal cortex value-related, regions.

Different cortical connectivities in human females and males relate to differences in strength and body composition, reward and emotional systems, and memory.

Sex differences in human brain structure and function are important, partly because they are likely to be relevant to the male-female differences in behavior and in mental health. To analyse sex differences in cortical function, functional connectivity was measured in 36,531 participants (53% female) in the UK Biobank (mean age 69) using the Human Connectome Project multimodal parcellation atlas with 360 well-specified cortical regions. Most of the functional connectivities were lower in females (Bonferroni corrected), with the mean Cohen's d = - 0.18. Removing these as covariates reduced the difference of functional connectivities for females-males from d = - 0.18 to - 0.06. The lower functional connectivities in females were especially of somatosensory/premotor regions including the insula, opercular cortex, paracentral lobule and mid-cingulate cortex, and were correlated with lower maximum workload (r = 0.17), and with higher whole body fat mass (r = - 0.17). But some functional connectivities were higher in females, involving especially the ventromedial prefrontal cortex and posterior cingulate cortex, and these were correlated with higher liking for some rewards such as sweet foods, higher happiness/subjective well-being, and with better memory-related functions. The main findings were replicated in 1000 individuals (532 females, mean age 29) from the Human Connectome Project. This investigation shows the cortical systems with different functional connectivity between females and males, and also provides for the first time a foundation for understanding the implications for behavior of these differences between females and males.

Hierarchical organization of the human ventral visual streams revealed with magnetoencephalography.

The hierarchical organization between 25 ventral stream visual cortical regions and 180 cortical regions was measured with magnetoencephalography using the Human Connectome Project Multimodal Parcellation atlas in 83 Human Connectome Project participants performing a visual memory task. The aim was to reveal the hierarchical organization using a whole-brain model based on generative effective connectivity with this fast neuroimaging method. V1-V4 formed a first group of interconnected regions. Especially V4 had connectivity to a ventrolateral visual stream: V8, the fusiform face cortex, and posterior inferior temporal cortex PIT. These regions in turn had effectivity connectivity to inferior temporal cortex visual regions TE2p and TE1p. TE2p and TE1p then have connectivity to anterior temporal lobe regions TE1a, TE1m, TE2a, and TGv, which are multimodal. In a ventromedial visual stream, V1-V4 connect to ventromedial regions VMV1-3 and VVC. VMV1-3 and VVC connect to the medial parahippocampal gyrus PHA1-3, which, with the VMV regions, include the parahippocampal scene area. The medial parahippocampal PHA1-3 regions have connectivity to the hippocampal system regions the perirhinal cortex, entorhinal cortex, and hippocampus. These effective connectivities of two ventral visual cortical streams measured with magnetoencephalography provide support to the hierarchical organization of brain systems measured with fMRI, and new evidence on directionality.

Topological arrangement of coronal segments in human callosal fibers in vivo tractography.

The topography of human callosal fibers in the midsagittal corpus callosum (mid-CC), in terms of cortical termination, is inconsistent in the literature. Despite being a high-profile and controversial topic, heterotopic callosal bundles (HeCBs) have not been studied from a whole-brain perspective. Here, we used multi-modal magnetic resonance imaging data from Human Connectome Project Development to explore these two topographic aspects by combining whole-brain tractography based on multi-shell multi-tissue constrained spherical deconvolution, the post-tractography reducing-false-positive-streamline algorithm of Convex Optimization Modeling for Microstructure Informed Tractography 2, and the new cortex parcellation atlas of Human Connectome Project multi-modal parcellation, version 1.0. We proposed that the callosal streamlines would demonstrate a topological arrangement of coronal segments arranged from anterior to posterior, with each perpendicular to the long axis of the mid-CC following its natural curvature, and the adjacent segments overlapping one another owing to the existence of HeCBs. We found that the cortices connected by the coronal segments, from anterior to posterior, corresponded exactly to the cortices from anterior to posterior in the flattened cortical surfaces of this atlas, indicating the original relative positions of the neocortex before curling and flipping during brain evolution. For each cortical area defined by this atlas, the sum strength of the HeCBs was far greater than that of the homotopic callosal bundle. Our findings on the topography of the whole CC would help in further understanding the network between the bilateral hemispheres and preventing disconnection syndromes in clinical settings.

Functional connectivity analysis of the depression connectome provides potential markers and targets for transcranial magnetic stimulation

BackgroundDespite efforts to improve targeting accuracy of the dorsolateral prefrontal cortex (DLPFC) as a repetitive transcranial magnetic stimulation (rTMS) target for Major Depressive Disorder (MDD), the heterogeneity in clinical response remains unexplained. ObjectiveWe sought to compare the patterns of functional connectivity from the DLPFC treatment site in patients with MDD who were TMS responders to those who were TMS non-responders. MethodsBaseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 37 participants before they underwent a course of rTMS to left Brodmann area 46. A novel machine learning method was utilized to identify brain regions associated with each item of the Beck's Depression Inventory II (BDI-II), and for 26 participants who underwent rTMS treatment over the left Brodmann area 46, identify regions differentiating rTMS responders and non-responders. ResultsNine parcels of the Human Connectome Project Multimodal Parcellation Atlas matched to at least three items of the Beck's Depression Inventory II (BDI-II) as predictors of response to rTMS, with many in the temporal, parietal and cingulate cortices. Additionally, pre-treatment mapping for 17 items of the BDI-II demonstrated significant variability in symptom to parcel mapping. When parcels associated with symptom presence and symptom resolution were compared, 15 parcels were uniquely associated with resolution (potential targets), and 12 parcels were associated with both symptom presence and resolution (blockers or biomarkers). ConclusionsMachine learning approaches show promise for the development of pathoanatomical diagnosis and treatment algorithms for MDD. Prospective studies are required to facilitate clinical translation.

Attentional M100 gain modulation localizes to auditory sensory cortex and is deficient in first-episode psychosis.

Selective attention is impaired in first-episode psychosis (FEP). Selective attention effects can be detected during auditory tasks as increased sensory activity. We previously reported electroencephalography scalp-measured N100 enhancement is reduced in FEP. Here, we localized magnetoencephalography (MEG) M100 source activity within the auditory cortex, making novel use of the Human Connectome Project multimodal parcellation (HCP-MMP) to identify precise auditory cortical areas involved in attention modulation and its impairment in FEP. MEG was recorded from 27 FEP and 31 matched healthy controls (HC) while individuals either ignored frequent standard and rare oddball tones while watching a silent movie or attended tones by pressing a button to oddballs. Because M100 arises mainly in the auditory cortices, MEG activity during the M100 interval was projected to the auditory sensory cortices defined by the HCP-MMP (A1, lateral belt, and parabelt parcels). FEP had less auditory sensory cortex M100 activity in both conditions. In addition, there was a significant interaction between group and attention. HC enhanced source activity with attention, but FEP did not. These results demonstrate deficits in both sensory processing and attentional modulation of the M100 in FEP. Novel use of the HCP-MMP revealed the precise cortical areas underlying attention modulation of auditory sensory activity in healthy individuals and impairments in FEP. The sensory reduction and attention modulation impairment indicate local and systems-level pathophysiology proximal to disease onset that may be critical for etiology. Further, M100 and N100 enhancement may serve as outcome variables for targeted intervention to improve attention in early psychosis.

Multigroup recognition of dementia patients with dynamic brain connectivity under multimodal cortex parcellation

ObjectiveTo accurately predict Alzheimer’s disease (AD) in its early stage of cognitive impairment is crucial to clinical diagnosis and intervention. However, there is no consensus over which parts of brain areas are responsible for cognitive decline due to the incompatible and single-modal-based parcellation methods employed by researchers. MethodsA novel dynamic brain connectivity processing method (DBCP) is proposed based on the human connectome project multimodal parcellation (HCP MMP) to explore the spatial–temporal characteristics of the brain in different stages of mild cognitive impairment (MCI) and Alzheimer’s disease. First, dynamic connectivity under HCP MMP is constructed to divide the whole fMRI time series into hundreds of segmentations. Then, graph-based topological measures are calculated, followed by statistical outlier examinations implemented by the K-means method. ResultsA superior performance (accuracy = 86%, recall = 87%, precision = 86%, F1-score = 86%) in the four groups (healthy control vs. early MCI vs. late MCI vs. AD) recognition is achieved by training an effective but uncomplicated deep learning model. ConclusionDynamic connectivity within the most fine-grained multimodal human cortex parcellation can reveal more useful details to distinguish brain dysfunctional patients compared with static connectivity or single modal based parcellation, and the proposed method can suppress the outliers well among fragmented fMRI signals. SignificanceProviding more evidence on the primary responsibility of DMN and DAN for cognitive impairment of the brain, 64 cortex regions with significant topological alterations are suggested as the most prominent and fine-grained biomarker for further longitudinal AD studies.

A Binary Classification Study of Alzheimer’s Disease Based on a Novel Subclass Weighted Logistic Regression Method

Based on proposed joint human connectome project multi-modal parcellation (JHCPMMP), the study on the binary classification of Alzheimer’s disease was conducted. We tried to build a novel classification model, which can be interpretative and have the ability to deal with the complexity and individual differences of brain networks. The subclass weighted logistic regression (SWLR) based on logistic regression was proposed in this paper. We conducted five groups of experiments, in which the accuracy of HC vs. AD was 95.8%, HC vs. EMCI was 91.6%, HC vs. LMCI was 93.7%, EMCI vs. LMCI was 89.5%, and LMCI vs. AD was 91.6%. In addition, we conducted a follow-up analysis of the coefficient matrix and found that the distribution of core deterioration brain regions in different stages is different in the development of Alzheimer’s disease. We located these brain regions in two-dimensional images and found that they generally show a trend of continuous counterclockwise migration.

The application of the Human Connectome Project in large scale brain network imaging: A potential biomarker for Alzheimer's disease and related dementias

AbstractBackgroundAlzheimer’s disease and related dementias (ADRD) result in progressive dysfunction of large scale brain networks (LSBNs) including the default mode network (DMN). Resting state functional MRI (rs‐fMRI) is an imaging tool offering the potential for tracking LSBN degeneration, but been underutilized as an imaging biomarker. Potential reasons include complexity of imaging protocols and analyzing imaging data. If these steps were simplified and operationalized, further integration of rs‐fMRI into clinical trials as a surrogate endpoint may be feasible. We describe an automated connectome mapping program (Infinitome) that leverages rs‐fMRI scans with standardized protocols, Human Connectome Project (HCP) data, and machine learning to understand longitudinal LSBN degeneration in dementia (figure 1).MethodThe Infinitome program creates a subject specific version of the Human Connectome Project Multimodal Parcellation (HCP‐MMP1) atlas using diffusion tractography (figure 2). Analytics are performed on both diffusion tensor imaging and rs‐fMRI. Outlier detection using a tangent space connectivity matrix is performed by comparing results with a subset of 300 normal HCP subject fMRI samples to determine the range of normal correlations for each regions of interest in a LSBN. Abnormal connectivity is determined as a 3‐sigma outlier for that correlation (figure 3). The imaging protocol for rs‐fMRI is limited to 8 minutes, enabling each study to be readily added on to structural MRI sequences. The program also provides automated image processing and analysis, thus simplifying the process of accessing imaging data and incorporating rs‐fMRI into routine practice.ResultPreliminary work has shown that connectomic analysis using this software in ADRD is feasible and can detect functional anomalies involving regions of interest described by the HCP.ConclusionBy providing support for fMRI processing through a cloud‐based server and incorporating normal control data from the HCP, the Infinitome program addresses the challenges associated with the analysis and interpretation of rs‐fMRI data. Thus, it has the potential to simplify the incorporation of rs‐fMRI into clinical trials as a biomarker for progressive neurodegeneration of vulnerable networks in ADRD. Further studies are planned evaluating LSBNs in AD and dementia with Lewy bodies.

An extended Human Connectome Project multimodal parcellation atlas of the human cortex and subcortical areas.

A modified and extended version, HCPex, is provided of the surface-based Human Connectome Project-MultiModal Parcellation atlas of human cortical areas (HCP-MMP v1.0, Glasser et al. 2016). The original atlas with 360 cortical areas has been modified in HCPex for ease of use with volumetric neuroimaging software, such as SPM, FSL, and MRIcroGL. HCPex is also an extended version of the original atlas in which 66 subcortical areas (33 in each hemisphere) have been added, including the amygdala, thalamus, putamen, caudate nucleus, nucleus accumbens, globus pallidus, mammillary bodies, septal nuclei and nucleus basalis. HCPex makes available the excellent parcellation of cortical areas in HCP-MMP v1.0 to users of volumetric software, such as SPM and FSL, as well as adding some subcortical regions, and providing labelled coronal views of the human brain.

Posterior Precuneus is Highly Connected to Medial Temporal Lobe Revealed by Tractography and White Matter Dissection

The precuneus, involved in various cognitive processes, is considered to form the midline core of the default mode network (DMN), while the medial temporal lobe (MTL) is a subsystem of the DMN. Until now, the anatomical study of the precuneus-MTL connection is limited in humans. One possible reason is the precuneus’ territory of the posteromedial cortex (PMC) is inconsistent across studies. The primary purpose of this study is to investigate the structural connectivity (SC) of precuneus-MTL, focusing on its anatomical organization using the Human Connectome Project Multi-modal Parcellation (HCP MMP) atlas. We first conducted the quantitative tractography analyses using the HCP dataset. The major streamlines originated from the posterior precuneus and were projected to the MTL extensively. Next, to complement the tractography data, we conducted the white matter dissection in the post-mortem human brain. We observed the major fiber bundles arise from the posterior precuneus extending to the anterior parahippocampal gyrus, which could support our tractography results. Then we analyzed the relationship between SC and resting-state functional connectivity (rsFC) of the precuneus-MTL. Although the SC-rsFC correlation was scarce on the whole, the posterior precuneus (POS2, 7Pm, 7m) showed a relatively high correlation (r = 0.38349, p < 0.05) with the posterior MTL (PreS, H, ProS, PHA1, PHA2). Our findings suggest the posterior precuneus is highly connected to MTL structurally, which could have an effect on the resting-state functional connectivity. In addition, the precuneus might consist of the heterogeneous connectivity-based subdivisions.

Pitch and Duration Mismatch Negativity are Associated With Distinct Auditory Cortex and Inferior Frontal Cortex Volumes in the First-Episode Schizophrenia Spectrum.

BackgroundPitch and duration mismatch negativity (pMMN/dMMN) are related to left Heschl’s gyrus gray matter volumes in first-episode schizophrenia (FESz). Previous methods were unable to delineate functional subregions within and outside Heschl’s gyrus. The Human Connectome Project multimodal parcellation (HCP-MMP) atlas overcomes this limitation by parcellating these functional subregions. Further, MMN has generators in inferior frontal cortex, and therefore, may be associated with inferior frontal cortex pathology. With the novel use of the HCP-MMP to precisely parcellate auditory and inferior frontal cortex, we investigated relationships between gray matter and pMMN and dMMN in FESz. MethodspMMN and dMMN were measured at Fz from 27 FESz and 27 matched healthy controls. T1-weighted MRI scans were acquired. The HCP-MMP atlas was applied to individuals, and gray matter volumes were calculated for bilateral auditory and inferior frontal cortex parcels and correlated with MMN. FDR correction was used for multiple comparisons. ResultsIn FESz only, pMMN was negatively correlated with left medial belt in auditory cortex and area 47L in inferior frontal cortex. Duration MMN negatively correlated with the following auditory parcels: left medial belt, lateral belt, parabelt, TA2, and right A5. Further, dMMN was associated with left area 47L, right area 44, and right area 47L in inferior frontal cortex. ConclusionsThe novel approach revealed overlapping and distinct gray matter associations for pMMN and dMMN in auditory and inferior frontal cortex in FESz. Thus, pMMN and dMMN may serve as biomarkers of underlying pathological deficits in both similar and slightly different cortical areas.

Parahippocampal area three gray matter is reduced in first-episode schizophrenia spectrum: Discovery and replication samples.

Early course schizophrenia is associated with reduced gray matter. The specific structures affected first and how deficits impact symptoms and cognition remain unresolved. We used the Human Connectome Project multimodal parcellation (HCP‐MMP) to precisely identify cortical areas and investigate thickness abnormalities in discovery and replication samples of first‐episode schizophrenia spectrum individuals (FESz). In the discovery sample, T1w scans were acquired from 31 FESz and 31 matched healthy controls (HC). Thickness was calculated for 360 regions in Freesurfer. In the replication sample, high‐resolution T1w, T2w, and BOLD‐rest scans were acquired from 23 FESz and 32 HC and processed with HCP protocols. Thickness was calculated for regions significant in the discovery sample. After FDR correction (q < .05), left and right parahippocampal area 3 (PHA3) were significantly thinner in FESz. In the replication sample, bilateral PHA3 were again thinner in FESz (q < .05). Exploratory correlation analyses revealed left PHA3 was positively associated with hallucinations and right PHA3 was positively associated with processing speed, working memory, and verbal learning. The novel use of the HCP‐MMP in two independent FESz samples revealed thinner bilateral PHA3, suggesting this byway between cortical and limbic processing is a critical site of pathology near the emergence of psychosis.

Identification of a distinct association fiber tract \u201cIPS-FG\u201d to connect the intraparietal sulcus areas and fusiform gyrus by white matter dissection and tractography

The intraparietal sulcus (IPS) in the posterior parietal cortex (PPC) is well-known as an interface for sensorimotor integration in visually guided actions. However, our understanding of the human neural network between the IPS and the cortical visual areas has been devoid of anatomical specificity. We here identified a distinctive association fiber tract “IPS-FG” to connect the IPS areas and the fusiform gyrus (FG), a high-level visual region, by white matter dissection and tractography. The major fiber bundles of this tract appeared to arise from the medial bank of IPS, in the superior parietal lobule (SPL), and project to the FG on the ventral temporal cortex (VTC) in post-mortem brains. This tract courses vertically at the temporo-parieto-occipital (TPO) junction where several fiber tracts intersect to connect the dorsal-to-ventral cortical regions, including the vertical occipital fasciculus (VOF). We then analyzed the structural connectivity of this tract with diffusion-MRI (magnetic resonance imaging) tractography. The quantitative tractography analysis revealed the major streamlines of IPS-FG interconnect the posterior IPS areas (e.g., IP1, IPS1) with FG (e.g., TF, FFC, VVC, PHA2, PIT) on the Human Connectome Project multimodal parcellation atlas (HCP MMP 1.0). Since the fronto-parietal network, including the posterior IPS areas, is recruited by multiple cognitive demands, the IPS-FG could play a role in the visuomotor integration as well as the top-down modulation of various cognitive functions reciprocally.

Determining the effects of LLD and MCI on brain decline according to machine learning and a structural covariance network analysis

BackgroundLate-life depression (LLD) and mild cognitive impairment (MCI) are risk factors for Alzheimer disease (AD). However, the interactive effect between LLD and MCI in the progression to AD remains unknown. The purpose of this research is to clarify whether this interaction exists and determined the characteristics of the structural change patterns in LLD and MCI. MethodTo address this question, a total 225 participants (91 with intact cognitive function (IC), 34 with MCI, 35 with LLD-IC, 47 with LLD-MCI and 18 with AD) were recruited for the current study and their T1 scanning were acquired. Machine learning was applied to estimate the brain's age gap according to grey matter information (thickness and volume was calculated based on the Human Connectome Project Multi-Modal Parcellation version 1.0 and the Desikan atlas). A structural covariance network (SCN) was constructed based on grey matter volume. Rich-club analysis, global network properties and the Jaccard distance were utilized to describe the topological features in each cohort. Their cognitive functions (executive function, processing speed and memory) were evaluated by a full-scale battery of neuropsychological tests. ResultThe interactive effect between LLD and MCI was detected through the brain age gap. The estimated age was positively correlated with processing speed and memory in LLD and non-LLD subjects. In the SCN analysis, the rich-club coefficient and global network properties were disrupted in the MCI group, but remained normal in the LLD-IC, LLD-MCI and AD groups. There was a significant discrepancy in brain structural change patterns between the AD and other cohorts by the Jaccard distance. ConclusionThe application of machine learning reflects that synergies between LLD and MCI could increase the risk of developing AD. According to the SCN, the structural coordination was disrupted in MCI and was kept normal in the other cohorts, while the discrepancies in brain structural change patterns appeared in AD. Overall, the brain age gap could be a potential predictor of AD, and the Jaccard distance has the potential to be a new type of SCN analysis indicator.

Parahippocampal Area 3 Gray Matter is Reduced in First-Episode Psychosis: A Discovery and Replication Study

The early course of schizophrenia is associated with reduced gray matter. It remains unclear which specific regions are impacted first. In two independent studies, we used the Human Connectome Project Multimodal Parcellation (HCP-MMP) to precisely parcellate cortex and investigate regional thickness abnormalities in first-episode psychosis individuals (FEP).

Alzheimer\u2019s disease, mild cognitive impairment, and normal aging distinguished by multi-modal parcellation and machine learning

A 360-area surface-based cortical parcellation is extended to study mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from healthy control (HC) using the joint human connectome project multi-modal parcellation (JHCPMMP) proposed by us. We propose a novel classification method named as JMMP-LRR to accurately identify different stages toward AD by integrating the JHCPMMP with the logistic regression-recursive feature elimination (LR-RFE). In three-group classification, the average accuracy is 89.0% for HC, MCI, and AD compared to previous studies using other cortical separation with the best classification accuracy of 81.5%. By counting the number of brain regions whose feature is in the feature subset selected with JMMP-LRR, we find that five brain areas often appear in the selected features. The five core brain areas are Fusiform Face Complex (L-FFC), Area 10d (L-10d), Orbital Frontal Complex (R-OFC), Perirhinal Ectorhinal (L-PeEc) and Area TG dorsal (L-TGd, R-TGd). The features corresponding to the five core brain areas are used to form a new feature subset for three classifications with the average accuracy of 80.0%. Results demonstrate the importance of the five core brain regions in identifying different stages toward AD. Experiment results show that the proposed method has better accuracy for the classification of HC, MCI, AD, and it also proves that the division of brain regions using JHCPMMP is more scientific and effective than other methods.

A novel joint HCPMMP method for automatically classifying Alzheimer’s and different stage MCI patients

A 360-area surface-based cortical parcellation was recently generated using multimodal data in a group average of 210 healthy young adults from the Human Connectome Project (HCP). In order to automatically and accurately identify mild cognitive impairment (MCI) at its two levels (early MCI and late MCI), Alzheimer's disease (AD) and healthy control (HC), a novel joint HCP MMP method was first proposed to delineate the cortical architecture and function connectivity in a group of non healthy adults. The proposed method was applied to register a dataset of 96 resting-state functional connectomes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to Connectivity Informatics Technology Initiative (CIFTI) space and parcellated brain into human connectome project multi-modal parcellation (HCPMMP) with 360 areas. Various network features in each node of the connectivity network were considered as the candidate features for classification.The fine-grained multi-modal based on HCP-MMP combined with machine learning in identification for EMCI, LMCI, AD and HC. Applying various network features, including strength, betweenness centrality, clustering coefficient, local efficiency, eigenvector centrality, etc, we trained and tested several machine learning models. Thousands of features were processed by filter and wrapper feature selection procedures, and finally there were thirty features to be selected to achieve classification accuracies of 93.8% for EMCI vs. HC, 95.8% for LMCI vs. HC, 95.8% for AD vs. HC, and 91.7% for LMCI vs. AD, respectively by using support vector machine (SVM) algorithm. Most of the selected features locate in the region of temporal or cingulate cortex. Compared with previous studies, our results demonstrate the superiority of the proposed method over existing techniques.

Shared genetic aetiology between cognitive performance and brain activations in language and math tasks

Cognitive performance is highly heritable. However, little is known about common genetic influences on cognitive ability and brain activation when engaged in a cognitive task. The Human Connectome Project (HCP) offers a unique opportunity to study this shared genetic etiology with an extended pedigree of 785 individuals. To investigate this common genetic origin, we took advantage of the HCP dataset, which includes both language and mathematics activation tasks. Using the HCP multimodal parcellation, we identified areals in which inter-individual functional MRI (fMRI) activation variance was significantly explained by genetics. Then, we performed bivariate genetic analyses between the neural activations and behavioral scores, corresponding to the fMRI task accuracies, fluid intelligence, working memory and language performance. We observed that several parts of the language network along the superior temporal sulcus, as well as the angular gyrus belonging to the math processing network, are significantly genetically correlated with these indicators of cognitive performance. This shared genetic etiology provides insights into the brain areas where the human-specific genetic repertoire is expressed. Studying the association of polygenic risk scores, using variants associated with human cognitive ability and brain activation, would provide an opportunity to better understand where these variants are influential.

Cortical atrophy patterns in early Parkinson's disease patients using hierarchical cluster analysis

IntroductionCortical brain atrophy detectable with MRI in non-demented advanced Parkinson's disease (PD) is well characterized, but its presence in early disease stages is still under debate. We aimed to investigate cortical atrophy patterns in a large sample of early untreated PD patients using a hypothesis-free data-driven approach. MethodsSeventy-seven de novo PD patients and 50 controls from the Parkinson's Progression Marker Initiative database with T1-weighted images in a 3-tesla Siemens scanner were included in this study. Mean cortical thickness was extracted from 360 cortical areas defined by the Human Connectome Project Multi-Modal Parcellation version 1.0, and a hierarchical cluster analysis was performed using Ward's linkage method. A general linear model with cortical thickness data was then used to compare clustering groups using FreeSurfer software. ResultsWe identified two patterns of cortical atrophy. Compared with controls, patients grouped in pattern 1 (n = 33) were characterized by cortical thinning in bilateral orbitofrontal, anterior cingulate, and lateral and medial anterior temporal gyri. Patients in pattern 2 (n = 44) showed cortical thinning in bilateral occipital gyrus, cuneus, superior parietal gyrus, and left postcentral gyrus, and they showed neuropsychological impairment in memory and other cognitive domains. ConclusionsEven in the early stages of PD, there is evidence of cortical brain atrophy. Neuroimaging clustering analysis is able to detect two subgroups of cortical thinning, one with mainly anterior atrophy, and the other with posterior predominance and worse cognitive performance.