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Article ; Online: Tracking spatial dynamics of functional connectivity during a task.

Wu, Lei / Caprihan, Arvind / Calhoun, Vince

2021 Volume 239, Page(s) 118310

Abstract: Functional connectivity (FC) measured from functional magnetic resonance imaging (fMRI) provides a powerful tool to explore brain organization. Studies of the temporal dynamics of brain organization have shown a large temporal variability of the ... ...

Abstract	Functional connectivity (FC) measured from functional magnetic resonance imaging (fMRI) provides a powerful tool to explore brain organization. Studies of the temporal dynamics of brain organization have shown a large temporal variability of the functional connectome, which may be associated with mental status transitions and/or adaptive process. Most dynamic studies, e.g. functional connectome and functional network connectivity (FNC), have focused on the macroscopic FC changes, i.e. the changes of temporal coherence across various brain network sources, nodes and/or regions of interest, where it is assumed within the network or node that the FC is static. In this paper, we develop a novel method to examine the spatial dynamics of FC, without the assumption of its intra-network stationarity. We applied our approach to fMRI data during an auditory oddball task (AOD) from twenty-two subjects, in an attempt to capture/validate the approach by evaluating whether spatial connectivity varies with task condition. The results showed that connectivity networks exhibit spatial variability over time, in addition to participating in conventional temporal dynamics, i.e. cross-network variability or dynamic functional network connectivity (dFNC). Furthermore, we studied the relationship of spatial dynamic in FC to cognitive processes, by performing a cluster analysis to evaluate an individual's functional correspondence towards the 'target' (oddball) detection from AOD task, and extracting cognitive task correspondence states as well as their dynamic FC spatial maps segregated by such states. We found a clear trend in different task-guided states, particularly, a prominent reduction of task stimulus synchrony state along with strong anticorrelation between default mode network (DMN) and cognitive attentional networks. We also observed an increasing occurrence of the task desynchrony state which showed an absence of DMN anticorrelation. The results highlight the impact of a well-studied cognitive task on the observed spatial dynamic structure. We also showed that the FC spatial dynamic pattern from our method largely corresponds to macroscopic dFNC patterns, but with more details and specifications over space, meanwhile the connectivity within the source itself provides novel information and varies over time. Overall, we demonstrate clear evidence of the presence of the (usually ignored) spatial dynamics of connectivity, its links to the task and implications of cognition/mental status.
MeSH term(s)	Acoustic Stimulation ; Adult ; Connectome/methods ; Default Mode Network/physiology ; Echo-Planar Imaging/methods ; Female ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Nerve Net/diagnostic imaging ; Nerve Net/physiology ; Psychomotor Performance/physiology ; Young Adult
Language	English
Publishing date	2021-06-24
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1147767-2
ISSN	1095-9572 ; 1053-8119
ISSN (online)	1095-9572
ISSN	1053-8119
DOI	10.1016/j.neuroimage.2021.118310
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Article: Multi-compartment diffusion magnetic resonance imaging models link tract-related characteristics with working memory performance in healthy older adults.

Bauer, Christopher E / Zachariou, Valentinos / Maillard, Pauline / Caprihan, Arvind / Gold, Brian T

Frontiers in aging neuroscience

2022 Volume 14, Page(s) 995425

Abstract: Multi-compartment diffusion MRI metrics [such as metrics from free water elimination diffusion tensor imaging (FWE-DTI) and neurite orientation dispersion and density imaging (NODDI)] may reflect more specific underlying white-matter tract ... ...

Abstract	Multi-compartment diffusion MRI metrics [such as metrics from free water elimination diffusion tensor imaging (FWE-DTI) and neurite orientation dispersion and density imaging (NODDI)] may reflect more specific underlying white-matter tract characteristics than traditional, single-compartment metrics [i.e., metrics from Diffusion Tensor Imaging (DTI)]. However, it remains unclear if multi-compartment metrics are more closely associated with age and/or cognitive performance than single-compartment metrics. Here we compared the associations of single-compartment [Fractional Anisotropy (FA)] and multi-compartment diffusion MRI metrics [FWE-DTI metrics: Free Water Eliminated Fractional Anisotropy (FWE-FA) and Free Water (FW); NODDI metrics: Intracellular Volume Fraction (ICVF), Orientation Dispersion Index (ODI), and CSF-Fraction] with both age and working memory performance. A functional magnetic resonance imaging (fMRI) guided, white matter tractography approach was employed to compute diffusion metrics within a network of tracts connecting functional regions involved in working memory. Ninety-nine healthy older adults (aged 60-85) performed an in-scanner working memory task while fMRI was performed and also underwent multi-shell diffusion acquisition. The network of white matter tracts connecting functionally-activated regions was identified using probabilistic tractography. Diffusion metrics were extracted from skeletonized white matter tracts connecting fMRI activation peaks. Diffusion metrics derived from both single and multi-compartment models were associated with age (
Language	English
Publishing date	2022-10-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2558898-9
ISSN	1663-4365
ISSN	1663-4365
DOI	10.3389/fnagi.2022.995425
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Article ; Online: A trichotomy method for defining homogeneous subgroups in a dementia population.

Caprihan, Arvind / Hillmer, Laura / Erhardt, Erik Barry / Adair, John C / Knoefel, Janice E / Prestopnik, Jillian / Rosenberg, Gary A

Annals of clinical and translational neurology

2023 Volume 10, Issue 10, Page(s) 1802–1815

Abstract: Introduction: Diagnosis of dementia in the aging brain is confounded by the presence of multiple pathologies. Mixed dementia (MX), a combination of Alzheimer's disease (AD) proteins with vascular disease (VD), is frequently found at autopsy, and has ... ...

Abstract	Introduction: Diagnosis of dementia in the aging brain is confounded by the presence of multiple pathologies. Mixed dementia (MX), a combination of Alzheimer's disease (AD) proteins with vascular disease (VD), is frequently found at autopsy, and has been difficult to diagnose during life. This report develops a method for separating the MX group and defining preclinical AD (presence of AD factors with normal cognition) and preclinical VD subgroups (presence of white matter damage with normal cognition). Methods: Clustering was based on three diagnostic axes: (1) AD factor (ADF) derived from cerebrospinal fluid proteins (Aβ42 and pTau), (2) VD factor (VDF) calculated from mean free water and peak width of skeletonized mean diffusivity in the white matter, and (3) Cognition (Cog) based on memory and executive function. The trichotomy method was applied to an Alzheimer's Disease Neuroimaging Initiative cohort (N = 538). Results: Eight biologically defined subgroups were identified which included the MX group with both high ADF and VDF (9.3%) and a preclinical VD group (3.9%), and a preclinical AD group (13.6%). Cog is significantly associated with both ADF and VDF, and the partial-correlation remains significant even when the effect of the other variable is removed (r(Cog, ADF/VDF removed) = 0.46, p < 10 Discussion: The trichotomy method creates eight biologically characterized patient groups, which includes MX, preclinical AD, and preclinical VD subgroups. Further longitudinal studies are needed to determine the utility of the 3-way clustering method with multimodal biological biomarkers.
MeSH term(s)	Humans ; Alzheimer Disease/pathology ; Brain/pathology ; Cognition ; Executive Function ; Aging
Language	English
Publishing date	2023-08-21
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2740696-9
ISSN	2328-9503 ; 2328-9503
ISSN (online)	2328-9503
ISSN	2328-9503
DOI	10.1002/acn3.51869
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Article ; Online: Review of diffusion MRI studies in chronic white matter diseases.

Raja, Rajikha / Rosenberg, Gary / Caprihan, Arvind

Neuroscience letters

2018 Volume 694, Page(s) 198–207

Abstract: Diffusion MRI studies characterizing the changes in white matter (WM) due to vascular cognitive impairment, which includes all forms of small vessel disease are reviewed. We reviewed the usefulness of diffusion methods in discriminating the affected WM ... ...

Abstract	Diffusion MRI studies characterizing the changes in white matter (WM) due to vascular cognitive impairment, which includes all forms of small vessel disease are reviewed. We reviewed the usefulness of diffusion methods in discriminating the affected WM regions and its relation to cognitive impairment. These studies were categorized based on the diffusion MRI techniques used. The most common method was the diffusion tensor imaging, whereas other methods included diffusion weighted imaging, diffusion kurtosis imaging, intravoxel incoherent motion, and studies based on diffusion tractography. The diffusion measures showed correlation with cognitive scores and disease progression, with mean diffusivity being the most robust parameter. Future studies should focus on incorporating multi-compartment and higher order diffusion models, which can handle the presence of multiple and crossing fibers inside a voxel.
MeSH term(s)	Anisotropy ; Cerebral Small Vessel Diseases/complications ; Chronic Disease ; Cognitive Dysfunction/complications ; Dementia, Vascular ; Diffusion Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Humans ; Leukoencephalopathies/complications ; Leukoencephalopathies/diagnostic imaging ; Leukoencephalopathies/pathology
Language	English
Publishing date	2018-12-06
Publishing country	Ireland
Document type	Journal Article ; Review
ZDB-ID	194929-9
ISSN	1872-7972 ; 0304-3940
ISSN (online)	1872-7972
ISSN	0304-3940
DOI	10.1016/j.neulet.2018.12.007
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Article ; Online: Blood-brain barrier disruption measured by albumin index correlates with inflammatory fluid biomarkers.

Hillmer, Laura / Erhardt, Erik B / Caprihan, Arvind / Adair, John C / Knoefel, Janice E / Prestopnik, Jill / Thompson, Jeffrey / Hobson, Sasha / Rosenberg, Gary A

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism

2022 Volume 43, Issue 5, Page(s) 712–721

Abstract: Blood-brain barrier (BBB) permeability can be measured by the ratio of albumin in cerebrospinal fluid (CSF) and blood and by dynamic contrast-enhanced MRI (DCEMRI). Albumin is a large molecule measured in CSF and blood to form the albumin index ( ... ...

Abstract	Blood-brain barrier (BBB) permeability can be measured by the ratio of albumin in cerebrospinal fluid (CSF) and blood and by dynamic contrast-enhanced MRI (DCEMRI). Albumin is a large molecule measured in CSF and blood to form the albumin index (Q
MeSH term(s)	Humans ; Blood-Brain Barrier/metabolism ; Albumins/cerebrospinal fluid ; Biomarkers/metabolism ; Inflammation/metabolism ; Cytokines/metabolism
Chemical Substances	Albumins ; Biomarkers ; Cytokines
Language	English
Publishing date	2022-12-15
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	604628-9
ISSN	1559-7016 ; 0271-678X
ISSN (online)	1559-7016
ISSN	0271-678X
DOI	10.1177/0271678X221146127
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Zs.A 1663: Show issues

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Article ; Online: Discriminating VCID subgroups: A diffusion MRI multi-model fusion approach.

Raja, Rajikha / Caprihan, Arvind / Rosenberg, Gary A / Rachakonda, Srinivas / Calhoun, Vince D

Journal of neuroscience methods

2020 Volume 335, Page(s) 108598

Abstract: Background: Vascular cognitive impairment and dementia (VCID) and Alzheimer's disease are predominant diseases among the aging population resulting in decline of various cognitive domains. Diffusion weighted MRI (DW-MRI) has been shown to be a promising ...

Abstract	Background: Vascular cognitive impairment and dementia (VCID) and Alzheimer's disease are predominant diseases among the aging population resulting in decline of various cognitive domains. Diffusion weighted MRI (DW-MRI) has been shown to be a promising aid in the diagnosis of such diseases. However, there are various models of DW-MRI and the interpretation of diffusion metrics depends on the model used in fitting data. Most previous studies are entirely based on parameters calculated from a single diffusion model. New method: We employ a data fusion framework wherein diffusion metrics from different models such as diffusion tensor imaging, diffusion kurtosis imaging and constrained spherical deconvolution model are fused using well known blind source separation approach to investigate white matter microstructural changes in population comprising of controls and VCID subgroups. Multiple comparisons between subject groups and prediction analysis using features from individual models and proposed fusion model are carried out to evaluate performance of proposed method. Results: Diffusion features from individual models successfully distinguished between controls and disease groups, but failed to differentiate between disease groups, whereas fusion approach showed group differences between disease groups too. WM tracts showing significant differences are superior longitudinal fasciculus, anterior thalamic radiation, arcuate fasciculus, optic radiation and corticospinal tract. Comparison with existing method: ROC analysis showed increased AUC for fusion (AUC = 0.913, averaged across groups and tracts) compared to that of uni-model features (AUC = 0.77) demonstrating increased sensitivity of proposed method. Conclusion: Overall our results highlight the benefits of multi-model fusion approach, providing improved sensitivity in discriminating VCID subgroups.
MeSH term(s)	Aged ; Cognitive Dysfunction ; Diffusion Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Humans ; Pyramidal Tracts ; White Matter/diagnostic imaging
Language	English
Publishing date	2020-01-28
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ZDB-ID	282721-9
ISSN	1872-678X ; 0165-0270
ISSN (online)	1872-678X
ISSN	0165-0270
DOI	10.1016/j.jneumeth.2020.108598
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Article ; Online: Comparison of automated and manual quantification methods for neuromelanin-sensitive MRI in Parkinson's disease.

Shaff, Nicholas / Erhardt, Erik / Nitschke, Stephanie / Julio, Kayla / Wertz, Christopher / Vakhtin, Andrei / Caprihan, Arvind / Suarez-Cedeno, Gerson / Deligtisch, Amanda / Richardson, Sarah Pirio / Mayer, Andrew R / Ryman, Sephira G

Human brain mapping

2023 Volume 45, Issue 1, Page(s) e26544

Abstract: Neuromelanin-sensitive magnetic resonance imaging quantitative analysis methods have provided promising biomarkers that can noninvasively quantify degeneration of the substantia nigra in patients with Parkinson's disease. However, there is a need to ... ...

Abstract	Neuromelanin-sensitive magnetic resonance imaging quantitative analysis methods have provided promising biomarkers that can noninvasively quantify degeneration of the substantia nigra in patients with Parkinson's disease. However, there is a need to systematically evaluate the performance of manual and automated quantification approaches. We evaluate whether spatial, signal-intensity, or subject specific abnormality measures using either atlas based or manually traced identification of the substantia nigra better differentiate patients with Parkinson's disease from healthy controls using logistic regression models and receiver operating characteristics. Inference was performed using bootstrap analyses to calculate 95% confidence interval bounds. Pairwise comparisons were performed by generating 10,000 permutations, refitting the models, and calculating a paired difference between metrics. Thirty-one patients with Parkinson's disease and 22 healthy controls were included in the analyses. Signal intensity measures significantly outperformed spatial and subject specific abnormality measures, with the top performers exhibiting excellent ability to differentiate patients with Parkinson's disease and healthy controls (balanced accuracy = 0.89; area under the curve = 0.81; sensitivity =0.86; and specificity = 0.83). Atlas identified substantia nigra metrics performed significantly better than manual tracing metrics. These results provide clear support for the use of automated signal intensity metrics and additional recommendations. Future work is necessary to evaluate whether the same metrics can best differentiate atypical parkinsonism, perform similarly in de novo and mid-stage cohorts, and serve as longitudinal monitoring biomarkers.
MeSH term(s)	Humans ; Parkinson Disease/pathology ; Sensitivity and Specificity ; Magnetic Resonance Imaging/methods ; Biomarkers/metabolism ; Substantia Nigra/metabolism ; Melanins
Chemical Substances	neuromelanin ; Biomarkers ; Melanins
Language	English
Publishing date	2023-12-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	1197207-5
ISSN	1097-0193 ; 1065-9471
ISSN (online)	1097-0193
ISSN	1065-9471
DOI	10.1002/hbm.26544
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Article ; Online: MRI measurements of Blood-Brain Barrier function in dementia: A review of recent studies.

Raja, Rajikha / Rosenberg, Gary A / Caprihan, Arvind

Neuropharmacology

2017 Volume 134, Issue Pt B, Page(s) 259–271

Abstract: Blood-brain barrier (BBB) separates the systemic circulation and the brain, regulating transport of most molecules to protect the brain microenvironment. Multiple structural and functional components preserve the integrity of the BBB. Several imaging ... ...

Abstract	Blood-brain barrier (BBB) separates the systemic circulation and the brain, regulating transport of most molecules to protect the brain microenvironment. Multiple structural and functional components preserve the integrity of the BBB. Several imaging modalities are available to study disruption of the BBB. However, the subtle changes in BBB leakage that occurs in vascular cognitive impairment and Alzheimer's disease have been less well studied. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is the most widely adopted non-invasive imaging technique for evaluating BBB breakdown. It is used as a significant marker for a wide variety of diseases with large permeability leaks, such as brain tumors and multiple sclerosis, to more subtle disruption in chronic vascular disease and dementia. DCE-MRI analysis of BBB includes both model-free parameters and quantitative parameters using pharmacokinetic modelling. We review MRI studies of BBB breakdown in dementia. The challenges in measuring subtle BBB changes and the state of the art techniques are initially examined. Subsequently, a systematic review comparing methodologies from recent in-vivo MRI studies is presented. Various factors related to subtle BBB permeability measurement such as DCE-MRI acquisition parameters, arterial input assessment, T
MeSH term(s)	Animals ; Blood-Brain Barrier/diagnostic imaging ; Blood-Brain Barrier/physiopathology ; Dementia/diagnostic imaging ; Humans ; Magnetic Resonance Imaging
Language	English
Publishing date	2017-10-28
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review ; Systematic Review
ZDB-ID	218272-5
ISSN	1873-7064 ; 0028-3908
ISSN (online)	1873-7064
ISSN	0028-3908
DOI	10.1016/j.neuropharm.2017.10.034
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Article ; Online: An approach to directly link ICA and seed-based functional connectivity: Application to schizophrenia.

Wu, Lei / Caprihan, Arvind / Bustillo, Juan / Mayer, Andrew / Calhoun, Vince

NeuroImage

2018 Volume 179, Page(s) 448–470

Abstract: Independent component analysis (ICA) and seed-based analyses are widely used techniques for studying intrinsic neuronal activity in task-based or resting scans. In this work, we show there is a direct link between the two, and show that there are some ... ...

Abstract	Independent component analysis (ICA) and seed-based analyses are widely used techniques for studying intrinsic neuronal activity in task-based or resting scans. In this work, we show there is a direct link between the two, and show that there are some important differences between the two approaches in terms of what information they capture. We developed an enhanced connectivity-matrix independent component analysis (cmICA) for calculating whole brain voxel maps of functional connectivity, which reduces the computational complexity of voxel-based connectivity analysis on performing many temporal correlations. We also show there is a mathematical equivalency between parcellations on voxel-to-voxel functional connectivity and simplified cmICA. Next, we used this cost-efficient data-driven method to examine the resting state fMRI connectivity in schizophrenia patients (SZ) and healthy controls (HC) on a whole brain scale and further quantified the relationship between brain functional connectivity and cognitive performances measured by the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) battery. Current results suggest that SZ exhibit a wide-range abnormality, primarily a decrease, in functional connectivity both between networks and within different network hubs. Specific functional connectivity decreases were associated with MATRICS performance deficits. In addition, we found that resting state functional connectivity decreases was extensively associated with aging regardless of groups. In contrast, there was no relationship between positive and negative symptoms in the patients and functional connectivity. In sum, we have developed a novel mathematical relationship between ICA and seed-based connectivity that reduces computational complexity, which has broad applicability, and showed a specific application of this approach to characterize connectivity changes associated with cognitive scores in SZ.
MeSH term(s)	Adult ; Brain/physiopathology ; Brain Mapping/methods ; Female ; Humans ; Image Interpretation, Computer-Assisted/methods ; Male ; Middle Aged ; Models, Neurological ; Models, Theoretical ; Nerve Net/physiology ; Schizophrenia/physiopathology
Language	English
Publishing date	2018-06-15
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1147767-2
ISSN	1095-9572 ; 1053-8119
ISSN (online)	1095-9572
ISSN	1053-8119
DOI	10.1016/j.neuroimage.2018.06.024
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Article: Inflammatory Biomarkers Aid in Diagnosis of Dementia.

Erhardt, Erik B / Adair, John C / Knoefel, Janice E / Caprihan, Arvind / Prestopnik, Jillian / Thompson, Jeffrey / Hobson, Sasha / Siegel, David / Rosenberg, Gary A

Frontiers in aging neuroscience

2021 Volume 13, Page(s) 717344

Abstract: Dual pathology of Alzheimer's disease (AD) and vascular cognitive impairment and dementia (VCID) commonly are found together at autopsy, but mixed dementia (MX) is difficult to diagnose during life. Biological criteria to diagnose AD have been defined, ... ...

Abstract	Dual pathology of Alzheimer's disease (AD) and vascular cognitive impairment and dementia (VCID) commonly are found together at autopsy, but mixed dementia (MX) is difficult to diagnose during life. Biological criteria to diagnose AD have been defined, but are not available for vascular disease. We used the biological criteria for AD and white matter injury based on MRI to diagnose MX. Then we measured multiple biomarkers in CSF and blood with multiplex biomarker kits for proteases, angiogenic factors, and cytokines to explore pathophysiology in each group. Finally, we used machine learning with the Random forest algorithm to select the biomarkers of maximal importance; that analysis identified three proteases, matrix metalloproteinase-10 (MMP-10), MMP-3 and MMP-1; three angiogenic factors, VEGF-C, Tie-2 and PLGF, and three cytokines interleukin-2 (IL-2), IL-6, IL-13. To confirm the clinical importance of the variables, we showed that they correlated with results of neuropsychological testing.
Language	English
Publishing date	2021-08-17
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2558898-9
ISSN	1663-4365
ISSN	1663-4365
DOI	10.3389/fnagi.2021.717344
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