Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures

Flor A. Espinoza, Jingyu Liu, Jennifer Ciarochi, Jessica A. Turner, Victor M. Vergara, Arvind Caprihan, Maria Misiura, Hans J. Johnson, Jeffrey D. Long, Jeremy H. Bockholt, Jane S. Paulsen, Vince Daniel Calhoun

Research output: Contribution to journalArticle

Abstract

Dynamic functional network connectivity (dFNC) is an expansion of traditional, static FNC that measures connectivity variation among brain networks throughout scan duration. We used a large resting-state fMRI (rs-fMRI) sample from the PREDICT-HD study (N = 183 Huntington disease gene mutation carriers [HDgmc] and N = 78 healthy control [HC] participants) to examine whole-brain dFNC and its associations with CAG repeat length as well as the product of scaled CAG length and age, a variable representing disease burden. We also tested for relationships between functional connectivity and motor and cognitive measurements. Group independent component analysis was applied to rs-fMRI data to obtain whole-brain resting state networks. FNC was defined as the correlation between RSN time-courses. Dynamic FNC behavior was captured using a sliding time window approach, and FNC results from each window were assigned to four clusters representing FNC states, using a k-means clustering algorithm. HDgmc individuals spent significantly more time in State-1 (the state with the weakest FNC pattern) compared to HC. However, overall HC individuals showed more FNC dynamism than HDgmc. Significant associations between FNC states and genetic and clinical variables were also identified. In FNC State-4 (the one that most resembled static FNC), HDgmc exhibited significantly decreased connectivity between the putamen and medial prefrontal cortex compared to HC, and this was significantly associated with cognitive performance. In FNC State-1, disease burden in HDgmc participants was significantly associated with connectivity between the postcentral gyrus and posterior cingulate cortex, as well as between the inferior occipital gyrus and posterior parietal cortex.

Original languageEnglish (US)
JournalHuman Brain Mapping
DOIs
StateAccepted/In press - Jan 1 2019
Externally publishedYes

Fingerprint

Huntington Disease
Mutation
Genes
Brain
Magnetic Resonance Imaging
Occipital Lobe
Parietal Lobe
Somatosensory Cortex
Putamen
Gyrus Cinguli
Prefrontal Cortex
Cluster Analysis
Healthy Volunteers

Keywords

  • dynamic functional network connectivity
  • group independent component analysis
  • Huntington's disease
  • resting-state fMRI

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

Cite this

Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures. / Espinoza, Flor A.; Liu, Jingyu; Ciarochi, Jennifer; Turner, Jessica A.; Vergara, Victor M.; Caprihan, Arvind; Misiura, Maria; Johnson, Hans J.; Long, Jeffrey D.; Bockholt, Jeremy H.; Paulsen, Jane S.; Calhoun, Vince Daniel.

In: Human Brain Mapping, 01.01.2019.

Research output: Contribution to journalArticle

Espinoza, FA, Liu, J, Ciarochi, J, Turner, JA, Vergara, VM, Caprihan, A, Misiura, M, Johnson, HJ, Long, JD, Bockholt, JH, Paulsen, JS & Calhoun, VD 2019, 'Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures', Human Brain Mapping. https://doi.org/10.1002/hbm.24504
Espinoza, Flor A. ; Liu, Jingyu ; Ciarochi, Jennifer ; Turner, Jessica A. ; Vergara, Victor M. ; Caprihan, Arvind ; Misiura, Maria ; Johnson, Hans J. ; Long, Jeffrey D. ; Bockholt, Jeremy H. ; Paulsen, Jane S. ; Calhoun, Vince Daniel. / Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures. In: Human Brain Mapping. 2019.
@article{7d42b6b1adcd4a26b9ce12878e0422f3,
title = "Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures",
abstract = "Dynamic functional network connectivity (dFNC) is an expansion of traditional, static FNC that measures connectivity variation among brain networks throughout scan duration. We used a large resting-state fMRI (rs-fMRI) sample from the PREDICT-HD study (N = 183 Huntington disease gene mutation carriers [HDgmc] and N = 78 healthy control [HC] participants) to examine whole-brain dFNC and its associations with CAG repeat length as well as the product of scaled CAG length and age, a variable representing disease burden. We also tested for relationships between functional connectivity and motor and cognitive measurements. Group independent component analysis was applied to rs-fMRI data to obtain whole-brain resting state networks. FNC was defined as the correlation between RSN time-courses. Dynamic FNC behavior was captured using a sliding time window approach, and FNC results from each window were assigned to four clusters representing FNC states, using a k-means clustering algorithm. HDgmc individuals spent significantly more time in State-1 (the state with the weakest FNC pattern) compared to HC. However, overall HC individuals showed more FNC dynamism than HDgmc. Significant associations between FNC states and genetic and clinical variables were also identified. In FNC State-4 (the one that most resembled static FNC), HDgmc exhibited significantly decreased connectivity between the putamen and medial prefrontal cortex compared to HC, and this was significantly associated with cognitive performance. In FNC State-1, disease burden in HDgmc participants was significantly associated with connectivity between the postcentral gyrus and posterior cingulate cortex, as well as between the inferior occipital gyrus and posterior parietal cortex.",
keywords = "dynamic functional network connectivity, group independent component analysis, Huntington's disease, resting-state fMRI",
author = "Espinoza, {Flor A.} and Jingyu Liu and Jennifer Ciarochi and Turner, {Jessica A.} and Vergara, {Victor M.} and Arvind Caprihan and Maria Misiura and Johnson, {Hans J.} and Long, {Jeffrey D.} and Bockholt, {Jeremy H.} and Paulsen, {Jane S.} and Calhoun, {Vince Daniel}",
year = "2019",
month = "1",
day = "1",
doi = "10.1002/hbm.24504",
language = "English (US)",
journal = "Human Brain Mapping",
issn = "1065-9471",
publisher = "Wiley-Liss Inc.",

}

TY - JOUR

T1 - Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures

AU - Espinoza, Flor A.

AU - Liu, Jingyu

AU - Ciarochi, Jennifer

AU - Turner, Jessica A.

AU - Vergara, Victor M.

AU - Caprihan, Arvind

AU - Misiura, Maria

AU - Johnson, Hans J.

AU - Long, Jeffrey D.

AU - Bockholt, Jeremy H.

AU - Paulsen, Jane S.

AU - Calhoun, Vince Daniel

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Dynamic functional network connectivity (dFNC) is an expansion of traditional, static FNC that measures connectivity variation among brain networks throughout scan duration. We used a large resting-state fMRI (rs-fMRI) sample from the PREDICT-HD study (N = 183 Huntington disease gene mutation carriers [HDgmc] and N = 78 healthy control [HC] participants) to examine whole-brain dFNC and its associations with CAG repeat length as well as the product of scaled CAG length and age, a variable representing disease burden. We also tested for relationships between functional connectivity and motor and cognitive measurements. Group independent component analysis was applied to rs-fMRI data to obtain whole-brain resting state networks. FNC was defined as the correlation between RSN time-courses. Dynamic FNC behavior was captured using a sliding time window approach, and FNC results from each window were assigned to four clusters representing FNC states, using a k-means clustering algorithm. HDgmc individuals spent significantly more time in State-1 (the state with the weakest FNC pattern) compared to HC. However, overall HC individuals showed more FNC dynamism than HDgmc. Significant associations between FNC states and genetic and clinical variables were also identified. In FNC State-4 (the one that most resembled static FNC), HDgmc exhibited significantly decreased connectivity between the putamen and medial prefrontal cortex compared to HC, and this was significantly associated with cognitive performance. In FNC State-1, disease burden in HDgmc participants was significantly associated with connectivity between the postcentral gyrus and posterior cingulate cortex, as well as between the inferior occipital gyrus and posterior parietal cortex.

AB - Dynamic functional network connectivity (dFNC) is an expansion of traditional, static FNC that measures connectivity variation among brain networks throughout scan duration. We used a large resting-state fMRI (rs-fMRI) sample from the PREDICT-HD study (N = 183 Huntington disease gene mutation carriers [HDgmc] and N = 78 healthy control [HC] participants) to examine whole-brain dFNC and its associations with CAG repeat length as well as the product of scaled CAG length and age, a variable representing disease burden. We also tested for relationships between functional connectivity and motor and cognitive measurements. Group independent component analysis was applied to rs-fMRI data to obtain whole-brain resting state networks. FNC was defined as the correlation between RSN time-courses. Dynamic FNC behavior was captured using a sliding time window approach, and FNC results from each window were assigned to four clusters representing FNC states, using a k-means clustering algorithm. HDgmc individuals spent significantly more time in State-1 (the state with the weakest FNC pattern) compared to HC. However, overall HC individuals showed more FNC dynamism than HDgmc. Significant associations between FNC states and genetic and clinical variables were also identified. In FNC State-4 (the one that most resembled static FNC), HDgmc exhibited significantly decreased connectivity between the putamen and medial prefrontal cortex compared to HC, and this was significantly associated with cognitive performance. In FNC State-1, disease burden in HDgmc participants was significantly associated with connectivity between the postcentral gyrus and posterior cingulate cortex, as well as between the inferior occipital gyrus and posterior parietal cortex.

KW - dynamic functional network connectivity

KW - group independent component analysis

KW - Huntington's disease

KW - resting-state fMRI

UR - http://www.scopus.com/inward/record.url?scp=85059630599&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85059630599&partnerID=8YFLogxK

U2 - 10.1002/hbm.24504

DO - 10.1002/hbm.24504

M3 - Article

C2 - 30618191

AN - SCOPUS:85059630599

JO - Human Brain Mapping

JF - Human Brain Mapping

SN - 1065-9471

ER -