Structural connectivity-based segmentation of the human entorhinal cortex

The medial (MEC) and lateral entorhinal cortex (LEC), widely studied in rodents, are well defined and characterized. In humans, however, the exact locations of their homologues remain uncertain. Previous functional magnetic resonance imaging (fMRI) studies have subdivided the human EC into posterior-medial (pmEC) and anterior-lateral (alEC) parts, but uncertainty remains about the choice of imaging modality and seed regions, in particular in light of a substantial revision of the classical model of EC connectivity based on novel insights from rodent anatomy. Here, we used structural, not functional imaging, namely diffusion tensor imaging (DTI) and probabilistic tractography to segment the human EC based on differential connectivity to other brain regions known to project selectively to MEC or LEC. We defined MEC as more strongly connected to presubiculum and retrosplenial cortex (RSC), and LEC as more strongly connected to distal CA1 and proximal subiculum (dCA1pSub) and orbitofrontal cortex (OFC). Although our DTI segmentation had a larger medial-lateral component than in previous fMRI studies, our results show that the human MEC and LEC homologues have a border oriented both towards the posterior-anterior and medial-lateral axes, supporting the differentiation between pmEC and alEC.

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Topographic diversity of structural connectivity in schizophrenia

10.1101/282145 ◽

2018 ◽

Author(s):

Hongtao Ruan ◽

Qiang Luo ◽

Lena Palaniyappan ◽

Chu-Chung Huang ◽

Chun-Yi Zac Lo ◽

...

Keyword(s):

Classification Accuracy ◽

Diffusion Tensor ◽

Structural Connectivity ◽

Brain Regions ◽

Common Pathway ◽

Cross Sectional ◽

Dsm Iv ◽

High Degree ◽

Pathophysiological Model ◽

Diffusion Tensor Imaging Dti

AbstractThe neurobiological heterogeneity of schizophrenia is widely accepted, but it is unclear how mechanistic differences converge to produce the observed phenotype. Establishing a pathophysiological model that accounts for both heterogeneity and phenotypic similarity is essential to inform stratified treatment approaches. In this cross-sectional diffusion tensor imaging (DTI) study, we recruited 77 healthy controls (HC), and 71 patients with DSM-IV diagnosis of schizophrenia (SCZ), and reconstructed the structural connectivity of 90 brain regions covering entire cerebral cortex. We first confirmed the heterogeneity in structural connectivity by showing a reduced inter-individual similarity in SCZ compared with HC. Moreover, we found it was not possible to cluster patients into subgroups with shared patterns of dysconnectivity, indicating a high degree of mechanistic divergence in schizophrenia. Instead of the strength of connectivity between any particular brain regions, we investigated the diversity (or statistically, the variance) of the topographic distribution of the strength was reduced. HC had higher topographic diversity in whole brain structural connectivity compared to the patient group(P = 2 × 10−6, T = 4.96, Cohen′S d = 0.87). In 62 of the 90 brain regions, the topographic diversity was significantly reduced in patients compared to controls after FDR correction (<0.05). When topographic diversity was used as a discriminant feature for classification between patients and controls, we significantly (P = 4.29 × 10−24) improved the classification accuracy to 79.6% (sensitivity 78.3%, specificity 81.3%). This finding suggests highly individualized pattern of structural dysconnectivity underlying the heterogeneity of schizophrenia converges to a convergent common pathway as reduced topographic diversity for the clinical construct of the disease.

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Diffusion tensor imaging (DTI) findings in schizophrenia: A review

European Psychiatry ◽

10.1016/s0924-9338(11)72665-7 ◽

2011 ◽

Vol 26 (S2) ◽

pp. 960-960

Author(s):

J.L. Villegas Martínez ◽

J.A. Blanco Garrote ◽

F. Uribe Ladrón de Cegama ◽

B. Arribas Simón ◽

G. Cabús Piñol

Keyword(s):

Diffusion Tensor Imaging ◽

Diffusion Tensor ◽

Cochrane Review ◽

Brain Structures ◽

Brain Regions ◽

Parahippocampal Gyrus ◽

First Episode ◽

Chronic Patients ◽

Brain White Matter ◽

Diffusion Tensor Imaging Dti

IntroductionDiffusion tensor imaging (DTI) is a magnetic resonance imaging technique that have increasingly being used for the non-invasive evaluation of brain white matter (WM) abnormalities. Several studies suggest that the normal integration of cerebral function may be compromised in schizophrenia. Abnormalities in WM tracts may be directly relevant for the neuropathology of schizophrenia.ObjetivesThe purpose of this review was to discuss recent DTI findings in schizophrenia and a methodologic analysis.MethodsThe literature search was performed with the search engine PubMed of the U.S. National Library of Medicine. Search strategy used was based on the Cochrane review technique, limited to the period between 1998 (first report on DTI and schizophrenia) and May 2010. And limited to ‘Title/Abstract’. The reference lists of these studies were used to identify additional studies.ResultsThere is a striking amount of heterogeneity in findings, probably by methodologic problems. Brain regions such as the cingulate bundle, corpus callosum, and regions within frontal and temporal WM have a proportionally larger number of positive findings across the studies. In addition, WM tracts as The superior longitudinal fasciculus, fronto-occipital longitudinal fasciculi, uncinate fasciculi, frontal longitudinal fasciculus and the arcuate fasciculus have also positive findings in patients with schizophrenia. Other brain structures as the cerebellar peduncles, the fornix, the hippocampus and parahippocampal gyrus, the thalamic and optic radiations have been evaluated and shown positive findings. However, these findings are not present in all studies. DTI abnormalities in first-episode patients are less robust than in chronic patients.ConclusionsRecent DTI findings further support the hypothesis of structural dysconnectivity in schizophrenia.

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Integrative Structural Brain Network Analysis In Diffusion Tensor Imaging

10.1101/129015 ◽

2017 ◽

Cited By ~ 1

Author(s):

Moo K. Chung ◽

Jamie L. Hanson ◽

Nagesh Adluru ◽

Andrew L. Alexander ◽

Richard J. Davidson ◽

...

Keyword(s):

Diffusion Tensor Imaging ◽

Diffusion Tensor ◽

Structural Connectivity ◽

Brain Regions ◽

Brain Network ◽

Node Degree ◽

Fiber Tracts ◽

White Matter Fiber Tracts ◽

Structural Brain Network ◽

Structural Networks

AbstractIn diffusion tensor imaging, structural connectivity between brain regions is often measured by the number of white matter fiber tracts connecting them. Other features such as the length of tracts or fractional anisotropy (FA) are also used in measuring the strength of connectivity. In this study, we investigated the effects of incorporating the number of tracts, the tract length and FA-values into the connectivity model. Using various node-degree based graph theory features, the three connectivity models are compared. The methods are applied in characterizing structural networks between normal controls and maltreated children, who experienced maltreatment while living in post-institutional settings before being adopted by families in the US.

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Structure supports function: informing directed and dynamic functional connectivity with anatomical priors

10.1101/2021.05.11.443529 ◽

2021 ◽

Author(s):

David Pascucci ◽

Maria Rubega ◽

Joan Rue-Queralt ◽

Sebastien Tourbier ◽

Patric Hagmann ◽

...

Keyword(s):

Functional Connectivity ◽

Diffusion Tensor ◽

Meta Analysis ◽

Structural Connectivity ◽

Brain Regions ◽

Noise Perturbation ◽

Eeg Data ◽

Functional Brain Networks ◽

Anatomical Priors ◽

And Diffusion

The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections: the lack of a direct structural link between two brain regions prevents direct functional interactions. Despite the intrinsic relationship between structural (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited, especially for electrophysiological data. In the present work, we propose a new linear adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. Our results show that SC priors increase the resilience of FC estimates to noise perturbation while promoting sparser networks under biologically plausible constraints. The proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new method for multimodal imaging and dynamic FC analysis.

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Connectivity changes in major depressive disorder after rTMS: a review of functional and structural connectivity data

Epidemiology and Psychiatric Sciences ◽

10.1017/s2045796021000482 ◽

2021 ◽

Vol 30 ◽

Author(s):

G. Schiena ◽

G. Franco ◽

A. Boscutti ◽

G. Delvecchio ◽

E. Maggioni ◽

...

Keyword(s):

Diffusion Tensor ◽

Repetitive Transcranial Magnetic Stimulation ◽

Small Sample Size ◽

Structural Connectivity ◽

Unipolar Depression ◽

Brain Regions ◽

Small Sample ◽

Anterior Cingulate ◽

Antidepressant Effect ◽

Dorsolateral Prefrontal

Abstract Aims In the search for effective therapeutic strategies for depression, repetitive transcranial magnetic stimulation (rTMS) emerged as a non-invasive, promising treatment. This is because the antidepressant effect of rTMS might be related to neuronal plasticity mechanisms possibly reverting connectivity alterations often observed in depression. Therefore, in this review, we aimed at providing an overview of the findings reported by studies investigating functional and structural connectivity changes after rTMS in depression. Methods A bibliographic search was conducted on PubMed, including studies that used unilateral, excitatory (⩾10 Hz) rTMS treatment targeted on the left dorsolateral prefrontal cortex (DLPFC) in unipolar depressed patients. Results The majority of the results showed significant TMS-induced changes in functional connectivity (FC) between areas important for emotion regulation, including the DLPFC and the subgenual anterior cingulate cortex, and among regions that are part of the major resting-state networks, such as the Default Mode Network, the Salience Networks and the Central Executive Network. Finally, in diffusion tensor imaging studies, it has been reported that rTMS appeared to increase fractional anisotropy in the frontal lobe. Limitations The small sample size, the heterogeneity of the rTMS stimulation parameters, the concomitant use of psychotropic drugs might have limited the generalisability of the results. Conclusions Overall, rTMS treatment induces structural and FC changes in brain regions and networks implicated in the pathogenesis of unipolar depression. However, whether these changes underlie the antidepressant effect of rTMS still needs to be clarified.

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Scan–rescan and inter-vendor reproducibility of neurite orientation dispersion and density imaging metrics

Neuroradiology ◽

10.1007/s00234-019-02350-6 ◽

2019 ◽

Vol 62 (4) ◽

pp. 483-494

Author(s):

Christina Andica ◽

Koji Kamagata ◽

Takuya Hayashi ◽

Akifumi Hagiwara ◽

Wataru Uchida ◽

...

Keyword(s):

Gray Matter ◽

Healthy Subjects ◽

Volume Fraction ◽

Diffusion Tensor ◽

Intraclass Correlation ◽

Region Of Interest ◽

Brain Regions ◽

Automatic Extraction ◽

Dispersion Index ◽

Diffusion Tensor Imaging Dti

Abstract Purpose The reproducibility of neurite orientation dispersion and density imaging (NODDI) metrics in the human brain has not been explored across different magnetic resonance (MR) scanners from different vendors. This study aimed to evaluate the scan–rescan and inter-vendor reproducibility of NODDI metrics in white and gray matter of healthy subjects using two 3-T MR scanners from two vendors. Methods Ten healthy subjects (7 males; mean age 30 ± 7 years, range 23–37 years) were included in the study. Whole-brain diffusion-weighted imaging was performed with b-values of 1000 and 2000 s/mm2 using two 3-T MR scanners from two different vendors. Automatic extraction of the region of interest was performed to obtain NODDI metrics for whole and localized areas of white and gray matter. The coefficient of variation (CoV) and intraclass correlation coefficient (ICC) were calculated to assess the scan–rescan and inter-vendor reproducibilities of NODDI metrics. Results The scan–rescan and inter-vendor reproducibility of NODDI metrics (intracellular volume fraction and orientation dispersion index) were comparable with those of diffusion tensor imaging (DTI) metrics. However, the inter-vendor reproducibilities of NODDI (CoV = 2.3–14%) were lower than the scan–rescan reproducibility (CoV: scanner A = 0.8–3.8%; scanner B = 0.8–2.6%). Compared with the finding of DTI metrics, the reproducibility of NODDI metrics was lower in white matter and higher in gray matter. Conclusion The lower inter-vendor reproducibility of NODDI in some brain regions indicates that data acquired from different MRI scanners should be carefully interpreted.

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Single Cortical Microinfarcts Lead to Widespread Microglia/Macrophage Migration Along the White Matter

Cerebral Cortex ◽

10.1093/cercor/bhaa223 ◽

2020 ◽

Vol 31 (1) ◽

pp. 248-266

Author(s):

Alisa Lubart ◽

Amit Benbenishty ◽

Hagai Har-Gil ◽

Hadas Laufer ◽

Amos Gdalyahu ◽

...

Keyword(s):

White Matter ◽

Diffusion Tensor ◽

Brain Regions ◽

Subcortical White Matter ◽

Dependent Manner ◽

Longitudinal Diffusion ◽

Distant Tissue ◽

Migratory Path ◽

Tissue Reorganization ◽

Diffusion Tensor Imaging Dti

Abstract Loss of cognitive function with aging is a complex and poorly understood process. Recently, clinical research has linked the occurrence of cortical microinfarcts to cognitive decline. Cortical microinfarcts form following the occlusion of penetrating vessels and are considered to be restricted to the proximity of the occluded vessel. Whether and how such local events propagate and affect remote brain regions remain unknown. To this end, we combined histological analysis and longitudinal diffusion tensor imaging (DTI), following the targeted-photothrombotic occlusion of single cortical penetrating vessels. Occlusions resulted in distant tissue reorganization across the mouse brain. This remodeling co-occurred with the formation of a microglia/macrophage migratory path along subcortical white matter tracts, reaching the contralateral hemisphere through the corpus callosum and leaving a microstructural signature detected by DTI-tractography. CX3CR1-deficient mice exhibited shorter trail lengths, differential remodeling, and only ipsilateral white matter tract changes. We concluded that microinfarcts lead to brain-wide remodeling in a microglial CX3CR1-dependent manner.

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Diffusion tensor imaging in schizophrenia

European Psychiatry ◽

10.1016/j.eurpsy.2007.12.004 ◽

2008 ◽

Vol 23 (4) ◽

pp. 255-273 ◽

Cited By ~ 103

Author(s):

Marinos Kyriakopoulos ◽

Theodoros Bargiotas ◽

Gareth J. Barker ◽

Sophia Frangou

Keyword(s):

Diffusion Tensor Imaging ◽

White Matter ◽

Diffusion Tensor ◽

Brain Regions ◽

Basic Principles ◽

Future Directions ◽

White Matter Abnormalities ◽

Non Invasive ◽

Brain White Matter ◽

Diffusion Tensor Imaging Dti

AbstractDiffusion tensor imaging (DTI) is a magnetic resonance imaging technique that is increasingly being used for the non-invasive evaluation of brain white matter abnormalities. In this review, we discuss the basic principles of DTI, its roots and the contribution of European centres in its development, and we review the findings from DTI studies in schizophrenia. We searched EMBASE, PubMed, PsychInfo, and Medline from February 1998 to December 2006 using as keywords ‘schizophrenia’, ‘diffusion’, ‘tensor’, and ‘DTI’. Forty studies fulfilling the inclusion criteria of this review were included and systematically reviewed. White matter abnormalities in many diverse brain regions were identified in schizophrenia. Although the findings are not completely consistent, frontal and temporal white matter seems to be more commonly affected. Limitations and future directions of this method are discussed.

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Differences in Cortical Representation and Structural Connectivity of Hands and Feet between Professional Handball Players and Ballet Dancers

Neural Plasticity ◽

10.1155/2016/6817397 ◽

2016 ◽

Vol 2016 ◽

pp. 1-17 ◽

Cited By ~ 19

Author(s):

Jessica Meier ◽

Marlene Sofie Topka ◽

Jürgen Hänggi

Keyword(s):

Grey Matter ◽

Diffusion Tensor ◽

Structural Connectivity ◽

Brain Regions ◽

Voxel Based Morphometry ◽

Ballet Dancers ◽

Intensive Training ◽

Sport Expertise ◽

Hands And Feet ◽

Structural Adaptations

It is known that intensive training and expertise are associated with functional and structural neuroadaptations. Most studies, however, compared experts with nonexperts; hence it is, specifically for sports, unclear whether the neuroplastic adaptations reported are sport-specific or sport-general. Here we aimed at investigating sport-specific adaptations in professional handball players and ballet dancers by focusing on the primary motor and somatosensory grey matter (GM) representation of hands and feet using voxel-based morphometry as well as on fractional anisotropy (FA) of the corticospinal tract by means of diffusion tensor imaging-based fibre tractography. As predicted, GM volume was increased in hand areas of handball players, whereas ballet dancers showed increased GM volume in foot areas. Compared to handball players, ballet dancers showed decreased FA in both fibres connecting the foot and hand areas, but they showed lower FA in fibres connecting the foot compared to their hand areas, whereas handball players showed lower FA in fibres connecting the hand compared to their foot areas. Our results suggest that structural adaptations are sport-specific and are manifested in brain regions associated with the neural processing of sport-specific skills. We believe this enriches the plasticity research in general and extends our knowledge of sport expertise in particular.

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Characteristics of mental health implications and plasma metabolomics in patients recently recovered from COVID-19

Translational Psychiatry ◽

10.1038/s41398-021-01426-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lian Yang ◽

Mei Zhou ◽

Lingli Li ◽

Ping Luo ◽

Wenliang Fan ◽

...

Keyword(s):

Mental Health ◽

Metabolic Profiling ◽

Diffusion Tensor ◽

Demographic Data ◽

Mean Diffusivity ◽

Brain Regions ◽

Cerebral White Matter ◽

Radial Diffusivity ◽

Amine Metabolism ◽

Diffusion Tensor Imaging Dti

AbstractThis study aimed to explore the associations between cerebral white matter (WM) alterations, mental health status, and metabolism in recovered COVID-19 patients. We included 28 recovered COVID-19 patients and 27 healthy controls between April 2020 and June 2020. Demographic data, the mental health scores, diffusion-tensor imaging (DTI) data, and plasma metabolomics were collected and compared between the two groups. Tract-based spatial statistics and graph theory approaches were used for DTI data analysis. Untargeted metabolomics analysis of the plasma was performed. Correlation analyses were performed between these characteristics. Recovered COVID-19 patients showed decreased fractional anisotropy, increased mean diffusivity and radial diffusivity values in widespread brain regions, and significantly lower global efficiency, longer shortest path length, and less nodal local efficiency in superior occipital gyrus (all, P < 0.05, Bonferroni corrected). Our results also demonstrated significantly different plasma metabolic profiling in recovered COVID-19 patients even at 3 months after their hospital discharge, which was mainly related to purine pathways, amino acids, lipids, and amine metabolism. Certain regions with cerebral WM alterations in the recovered patients showed significant correlations with different metabolites and the mental health scores. We observed multiple alterations in both WM integrity and plasma metabolomics that may explain the deteriorated mental health of recovered COVID-19 patients. These findings may provide potential biomarkers for the mental health evaluation for the recovered COVID-19 patients and potential targets for novel therapeutics.

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