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Original Investigation |

Salience Network Resting-State Activity Prediction of Frontotemporal Dementia Progression FREE

Gregory S. Day, MD, MSc1; Norman A. S. Farb, PhD2; David F. Tang-Wai, MDCM1,3; Mario Masellis, MD, PhD1,4,5; Sandra E. Black, MD1,2,4; Morris Freedman, MD1,2,6; Bruce G. Pollock, MD5,7; Tiffany W. Chow, MD1,2,5,6,7
[+] Author Affiliations
1Department of Medicine (Neurology), University of Toronto, Toronto, Ontario, Canada
2Rotman Research Institute, Baycrest Health Science Centre, Toronto, Ontario, Canada
3University Health Network Memory Clinic, Toronto Western Hospital, Toronto, Ontario, Canada
4Brain Sciences Research Program, Sunnybrook Research Institute, and Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
5Centre for Addiction and Mental Health, Toronto, Ontario, Canada
6Division of Neurology, Department of Medicine, Baycrest and Mount Sinai Hospital, Toronto, Ontario, Canada
7Division of Geriatric Psychiatry, University of Toronto, Toronto, Ontario, Canada
JAMA Neurol. 2013;70(10):1249-1253. doi:10.1001/jamaneurol.2013.3258.
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Published online

Importance  Noninvasive measures of activity within intrinsic brain networks may be clinically relevant, providing a marker of neurodegenerative disease and predicting clinical behaviors.

Objective  To correlate baseline resting-state measures within the salience network and changes in behavior among patients with frontotemporal dementia.

Design  Baseline resting-state functional magnetic resonance imaging data and longitudinal clinical measures were obtained from prospectively accrued patients during 8 weeks.

Setting  Tertiary academic care center specializing in the assessment and management of patients with neurodegenerative disease.

Participants  Fifteen patients with clinically diagnosed frontotemporal dementia (5 behavioral variant and 10 semantic dementia).

Main Outcomes and Measures  Baseline resting-state functional magnetic resonance imaging data measured within regions of interest were regressed on serial behavioral measures from prospectively accrued patients with frontotemporal dementia to determine the ability of baseline resting-state activity to account for changes in behavior.

Results  Low-frequency fluctuations in the left insula significantly predicted changes in Frontal Behavioral Inventory scores (standard β = 0.51, P = .049), accounting for 28% of the change variance. The trend was driven by changes in measures of apathy independent of dementia severity.

Conclusion and Relevance  Baseline measures of salience network connectivity involving the left insula may predict behavioral changes in patients with frontotemporal dementia.

Figures in this Article

Advances in functional neuroimaging have provided a window into the brain’s intrinsic connectivity, leading to the discovery of the salience network (SLN), composed of the anterior cingulate, insula, striatum, and amygdala. The SLN is activated in healthy patients during tasks requiring attentional selection, task switching, and self-regulation of behavior1 and is an important neural substrate in frontotemporal dementia (FTD),2 with dysfunction confirmed on histopathology3 and resting-state functional magnetic resonance (fMR) imaging.1,2 Within the SLN, the insula has emerged as a nodal point of particular importance for frontolimbic function and dysfunction.2 Supporting this assertion, insular atrophy is recognized as one of the earliest structural biomarkers in behavioral variant FTD (bvFTD) and semantic dementia,3,4 with insular loss correlated with worsening behavioral inventory scores5 and progressive accumulation of FTD-associated pathologic inclusions within insular von Economo neurons and Fork cells.6,7

Abnormal activity within intrinsic brain networks may be clinically relevant, indicative of neurodegenerative disease.1,2 Resting-state fMR imaging may provide a noninvasive biomarker for the diagnosis and longitudinal monitoring of patients with FTD. However, it remains to be determined whether this emerging technique can be used to identify patterns of network disruption in patients before the development of changes on clinical examination or structural neuroimaging. We explored the ability of baseline resting-state connectivity measures to predict behavioral changes in participants with bvFTD and semantic dementia during 8 weeks.

All procedures were approved by institutional ethics boards. Written informed consent was obtained from patients or their substitute decision makers. Baseline resting-state fMR imaging data were collected from 15 patients with clinically diagnosed FTD (5 bvFTD and 10 semantic dementia8) before initiation of study medication as part of the protocol for a prospective open-label clinical trial. Details concerning study recruitment have been published previously.9 In the present study, the subtype diagnosis for 2 patients has been amended from bvFTD to semantic dementia because symptoms of the semantic variant of primary progressive aphasia manifested after clinical trial completion. Control participants were not explicitly recruited for the open-label trial; however, resting-state data were available from 16 age-matched healthy volunteers enlisted for a parallel study and were included in comparison analyses.1 Control participants did not differ from the patients in age, sex, or educational status. The term frontotemporal dementia refers herein to both the bvFTD and semantic dementia subtypes of FTD.

Behavioral measures were obtained at baseline and at 8 weeks from patients with FTD using the Frontal Behavioral Inventory (FBI) total score (with apathy and disinhibition subscores)10 and the more global Clinical Dementia Rating.11 Patients with FTD received the clinical intervention, memantine hydrochloride (10 mg), twice daily.

The fMR imaging protocol directed participants to lie with eyes closed during image acquisition. Data were preprocessed using a computer program (Data Processing Assistant for Resting-State fMRI; www.restfmri.net). To identify SLN hubs for our analysis, we compared resting-state activity in patients with FTD with that of healthy control subjects and selected regions of interest (ROIs) based on areas of maximal group distinction. Three ROIs were identified, including the right and left insulae and the medial anterior cingulate cortex extending into both hemispheres (eTable 1 in the Supplement).1

Resting-state activity within the right insula, left insula, and anterior cingulate ROIs was separately assessed using 2 distinct measures of voxelwise signal power and homogeneity. Signal power was measured using fractional amplitude of low-frequency fluctuation (fALFF),12 a voxelwise ratio between low-frequency power (ie, 0.01-0.1 Hz) and the broader-frequency spectrum of resting-state activity (ie, 0-0.25 Hz). The fALFF reported for an ROI is the first eigenvariate of the fALFF scores from all voxels within that region. Fractional amplitude of low-frequency fluctuation has emerged as a functional measure of local signal strength of connections within neural networks, providing a measure of integrity (ie, health) of individual nodal points within a network. On the other hand, regional homogeneity (REHO) provides a measure of local coherence in the brain, calculated as the cross-correlation between each voxel and its neighbors, reflecting coherence within an ROI.13 When applied to analysis of the spontaneous low-frequency fluctuations observed during the resting state, REHO is argued to represent local brain network integrity.14 Changes in fALFF and REHO within SLN structures can reliably discriminate patients having neurodegenerative disease from healthy control subjects.1,2

A forward linear regression analysis used baseline resting-state scores (REHO and fALFF) within ROIs to predict percentage change on behavioral scales at the end of 8 weeks. Before the forward regression, baseline FBI total scores were entered as a first step in the model to control for variation in initial symptom severity. A predictor variable was selected for inclusion in the model if it improved the model fit at a significance level of P < .05. Post hoc linear regression was performed to quantify the extent to which baseline resting-state activity predicted behavioral change in patients with FTD. Analyses were performed using statistical software (IBM SPSS Statistics 20; IBM Corporation).

Table 1 lists demographic and clinical characteristics of the patients; all had dementia of mild to moderate severity. The groups with bvFTD and semantic dementia did not differ in clinical or demographic measures. Changes in patients’ FBI scores were heterogeneous across 8 weeks, without discernible improvement or degradation (Table 2). Resting-state measures within the left insula differentiated controls from patients with semantic dementia and bvFTD (Figure).

Table Graphic Jump LocationTable 1.  Demographic and Clinical Characteristics of Study Patients
Table Graphic Jump LocationTable 2.  Frontal Behavioral Inventory (FBI) Baseline Scores and Percentage Change at 8 Weeks for Individual Patients
Place holder to copy figure label and caption
Figure.
Left Insula Fractional Amplitude of Low-frequency Fluctuation (fALFF) Scores and Symptom Change

A, Identification of left insula fALFF in patients having behavioral variant frontotemporal dementia (bvFTD) or semantic dementia vs healthy control subjects. B, Patient Frontal Behavioral Inventory (FBI) changes during 8 weeks and fALFF measures. The line represents the regression for the entire group (n = 15). The FBI change scores are residualized, controlling for baseline FBI scores. C, The FBI apathy subscore changes during 8 weeks and fALFF measures. D, The FBI disinhibition subscore changes and fALFF measures. Dashed vertical line indicates the mean fALFF activity measured in the control group.

Graphic Jump Location

Forward linear regression for the entire sample revealed a predictive relationship between fALFFs in the left insula and changes in behaviors captured with the FBI total scores (standard β = 0.51, P = .049) (eTable 2 in the Supplement). Higher left insular fALFF activity predicted an interval worsening (increase) in FBI scores, accounting for 28% of the change variance (Figure). The trend seemed to be driven by alterations in the FBI apathy subscores. Left insula fALFF predicted increases in the apathy subscores (standard β = 0.66, P = .006). Right insula resting-state measures did not independently predict changes in overall FBI scores; however, after controlling for left fALFF, right fALFF measures improved predictions of changes in the apathy subscores, while higher right insula fALFF identified those least likely to experience increases in the apathy subscores (standard β = −0.49, P = .03). Neither insular REHO nor resting-state measures within the anterior cingulate cortex accounted for changes in behavior. In addition, no correlation was observed between baseline resting-state measures and duration of illness, global Clinical Dementia Rating, or the magnitude of FBI scores (eTable 3 in the Supplement).

To confirm the generalization of these findings to FTD subtypes, we repeated the regression analysis separately for patients with bvFTD and semantic dementia. The results were more robust for the bvFTD group: baseline fluctuations in low-frequency resting-state activity in the left insula strongly predicted increases in the FBI (standard β = 1.02, P = .03, R2 = 0.80), especially increases in the apathy subscores (standard β = 1.07, P = .04, R2 = 0.85) (eTable 4 in the Supplement). A similar correlation with left fALFF activity was observed in the semantic dementia group (standard β = 0.61, P = .04, R2 = 0.37), including apathy subscores (standard β = 0.72, P = .02, R2 = 0.52) (eTable 5 in the Supplement). Correlations were confirmed with parametric and nonparametric statistical measures (eTable 6 in the Supplement), suggesting that the described relationship was not driven by outliers.

Patterns of connectivity within and between SLN structures reliably distinguish patients with FTD from healthy control subjects1 and from patients with Alzheimer disease.2 The results of this study corroborate the importance of the insula within the SLN, suggesting that baseline measures of SLN connectivity involving the left insula may predict changes in behavior in patients with FTD, as measured with the FBI. Measures of low-frequency signal within the left insula did not serve as an indicator of disease severity because no association was observed between baseline measures of resting-state activity and clinical features or measures of disease severity at the time of entry into the trial.

Rapid behavioral change early in the course of FTD is a well-described yet poorly understood clinical phenomenon.15 This effect is not seen in patients with Alzheimer disease,15 suggesting that accelerated functional decline in FTD may be explained by a model of neurodegenerative disease that emphasizes breakdown of network connectivity, preceding structural changes detected on standard neuroimaging. The pattern of breakdown is presumed to be distinct from that seen in Alzheimer disease, accounting for differences in clinical progression and facilitating differentiation between FTD and Alzheimer disease with resting-state measures.2,3 In line with this, the increased tonic signaling measured within the left insula of study patients may reflect a compensatory response resulting from loss of regional connections. Similar increases are recorded in the mean firing rate of neurons within the subthalamic nuclei of patients with Parkinson disease undergoing deep brain electrode implantation,16 suggesting that hyperactive neuronal discharges in the subthalamic nucleus are associated with motor dysfunction. The magnitude of resting-state activity measured within the SLN of our patients with FTD was less than that measured in healthy controls, emphasizing the importance of interpreting resting-state measures relative to other patients with FTD. In our study, a relative increase in fALFFs was predictive of behavioral worsening during as short a period as the next 8 weeks in patients with FTD. Within this population, measures of fALFFs within the left insula may provide a marker of a dysregulated network at greatest risk of collapse. An alternate explanation for the observed correlations may be that higher left insula resting-state activity selected for patients with a less advanced clinical stage of dementia, identifying those with preserved behavioral functions and the greatest potential for change (ie, the most to lose). However, no correlation was found between resting-state activity and clinical measures approximating disease severity, favoring the assertion that resting-state measures may predict behavioral change independent of assessable clinical measures. Resting-state measures may provide a functional neuroimaging correlate for the precipitous behavioral decline detailed in FTD,15 permitting evaluation of the role of SLN dysfunction in this process.

Anterior insular atrophy is reported early in the course of bvFTD, with the extent of right hemisphere involvement exceeding that of the left on structural neuroimaging.17 Correspondingly, fALFFs are reduced in the right insula of patients having bvFTD and semantic dementia compared with controls.1 Extending these findings, higher right insula fALFF seemed protective against worsening of apathy in patients with FTD (after controlling for left fALFF activity). Relative preservation of right insula fALFFs may be protective against behavioral decline. The contributions of dysfunction within right and left SLNs to the FTD phenotype are deserving of further study.

The small sample size in this study likely limited the ability to draw correlations between resting-state measures and behavior. No significant changes in behavioral measures were reported across the 8-week study period for the total sample, the bvFTD group, or the semantic dementia group, indicating that a longer study with a larger sample might be more informative. In addition, all patients received memantine hydrochloride. However, it is unlikely that open-label use of memantine significantly altered behavior given the published randomized control trial that demonstrated no effect in patients with FTD,18 as well as the finding that FBI scores did not improve during the open-label trial study period.9 Future studies could control for medication use and include more resting-state measurements during the course of the illness. Eight weeks may be too short a time to detect clinically significant changes in network connectivity.

Limitations notwithstanding, the results of this analysis expand on prior studies. Resting-state measures of neural connectivity may provide a noninvasive means of assessing network functioning in neurodegenerative disease.

Accepted for Publication: April 23, 2013.

Corresponding Author: Gregory S. Day, MD, MSc, Division of Neurology, University of Toronto, Toronto Western Hospital, 399 Bathurst St, Toronto, ON M5T 2S8, Canada (gregg.day@mail.utoronto.ca)

Published Online: August 19, 2013. doi:10.1001/jamaneurol.2013.3258.

Author Contributions:Study concept and design: Farb, Pollock, Chow.

Acquisition of data: Pollock, Chow.

Analysis and interpretation of data: Day, Farb, Tang-Wai, Masellis, Black, Freedman, Chow.

Drafting of the manuscript: Day, Farb, Chow.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Day, Farb.

Obtained funding: Farb, Freedman, Chow.

Administrative, technical, and material support: Chow.

Study supervision: Tang-Wai, Chow.

Conflict of Interest Disclosures: Dr Tang-Wai holds a grant with the Weston Foundation and is a collaborator on grants from the Canadian Institutes of Health Research, Alzheimer Society of Canada, Parkinson Society of Canada, and The Michael J. Fox Foundation for Parkinson’s Research. Dr Masellis has received speaker honoraria from Novartis and EMD Serono, Inc; serves as an associate editor for Current Pharmacogenomics and Personalized Medicine; receives publishing royalties from Henry Stewart Talks; has served as a consultant for Bioscape Medical Imaging CRO; and receives research support from the Canadian Institutes of Health Research, Parkinson Society Canada; an Early Researcher Award from the Ministry of Economic Development and Innovation of Ontario, The Consortium of Canadian Centres for Clinical Cognitive Research, and Teva Pharmaceutical Industries Ltd. Dr Black holds grants from the Weston Foundation, Brain Canada, Canadian Institutes of Health, Heart and Stroke Foundation, and Heart and Stroke Foundation Centre for Stroke Recovery, as well as research contracts with Roche, Pfizer, Elan, and Glaxo Smith Kline; she has served as an ad hoc consultant for Roche, Bristol-Myers Squibb, Pfizer, Novartis, and Elan and has presented continuing medical education sponsored by Pfizer, Eisai, and Novartis. Dr Freedman has served on an advisory board for Novartis, has consulted with Bristol-Myers Squibb, received financial support for a behavioral neurology fellow from Eli Lilly Canada, receives royalties for a book on clock drawing from Oxford University Press, and is listed on a provisional patent related to methods and kits for the differential diagnosis of Alzheimer disease vs frontotemporal dementia using blood biomarkers and may be listed on the planned patent application. Dr Chow received support for collection of the resting-state data used in this study from an investigator-initiated trial grant from Lundbeck Canada. No other disclosures were reported.

Funding/Support: This work was supported by grants from Women of Baycrest and the Moir Family (Drs Farb and Chow), the Sam and Ida Ross Memory Clinic (Drs Freedman and Chow), the Saul A. Silverman Family Foundation as a Canada International Scientific Exchange Program, and Morris Kerzner Memorial Fund (Dr Freedman), and through an investigator-initiated trial grant from Lundbeck Canada (Dr Chow).

Farb  NA, Grady  CL, Strother  S,  et al.  Abnormal network connectivity in frontotemporal dementia: evidence for prefrontal isolation. Cortex. 2013;49(7):1856-1873.
PubMed   |  Link to Article
Zhou  J, Greicius  MD, Gennatas  ED,  et al.  Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer’s disease. Brain. 2010;133(pt 5):1352-1367.
PubMed   |  Link to Article
Seeley  WW, Crawford  RK, Zhou  J, Miller  BL, Greicius  MD.  Neurodegenerative diseases target large-scale human brain networks. Neuron. 2009;62(1):42-52.
PubMed   |  Link to Article
Brambati  SM, Rankin  KP, Narvid  J,  et al.  Atrophy progression in semantic dementia with asymmetric temporal involvement: a tensor-based morphometry study. Neurobiol Aging. 2009;30(1):103-111.
PubMed   |  Link to Article
Seeley  WW, Allman  JM, Carlin  DA,  et al.  Divergent social functioning in behavioral variant frontotemporal dementia and Alzheimer disease: reciprocal networks and neuronal evolution. Alzheimer Dis Assoc Disord. 2007;21(4):S50-S57.
PubMed   |  Link to Article
Kim  EJ, Sidhu  M, Gaus  SE,  et al.  Selective frontoinsular von Economo neuron and fork cell loss in early behavioral variant frontotemporal dementia. Cereb Cortex. 2012;22(2):251-259.
PubMed   |  Link to Article
Chow  TW, Links  KA, Masterman  DL, Mendez  MF, Vinters  HV.  A case of semantic variant primary progressive aphasia with severe insular atrophy. Neurocase. 2012;18(6):450-456.
PubMed   |  Link to Article
Neary  D, Snowden  JS, Gustafson  L,  et al.  Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology. 1998;51(6):1546-1554.
PubMed   |  Link to Article
Chow  TW, Graff-Guerrero  A, Verhoeff  NP,  et al.  Open-label study of the short-term effects of memantine on FDG-PET in frontotemporal dementia. Neuropsychiatr Dis Treat. 2011;7:415-424.
PubMed   |  Link to Article
Kertesz  A, Davidson  W, Fox  H.  Frontal Behavioral Inventory: diagnostic criteria for frontal lobe dementia. Can J Neurol Sci.1997;24(1):29-36.
Burke  WJ, Miller  JP, Rubin  EH,  et al.  Reliability of the Washington University Clinical Dementia Rating. Arch Neurol. 1988;45(1):31-32.
PubMed   |  Link to Article
Zou  QH, Zhu  CZ, Yang  Y,  et al.  An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods. 2008;172(1):137-141.
PubMed   |  Link to Article
Zang  Y, Jiang  T, Lu  Y, He  Y, Tian  L.  Regional homogeneity approach to fMRI data analysis. Neuroimage. 2004;22(1):394-400.
PubMed   |  Link to Article
Zou  Q, Wu  CW, Stein  EA, Zang  Y, Yang  Y.  Static and dynamic characteristics of cerebral blood flow during the resting state. Neuroimage. 2009;48(3):515-524.
PubMed   |  Link to Article
Chow  TW, Fridhandler  JD, Binns  MA,  et al.  Trajectories of behavioral disturbance in dementia. J Alzheimers Dis. 2012;31(1):143-149.
PubMed
Steigerwald  F, Pötter  M, Herzog  J,  et al.  Neuronal activity of the human subthalamic nucleus in the parkinsonian and nonparkinsonian state. J Neurophysiol. 2008;100(5):2515-2524.
PubMed   |  Link to Article
Seeley  WW, Crawford  RK, Rascovsky  K,  et al.  Frontal paralimbic network atrophy in very mild behavioral variant frontotemporal dementia. Arch Neurol. 2008;65(2):249-255.
PubMed   |  Link to Article
Boxer  AL, Knopman  DS, Kaufer  DI,  et al.  Memantine in patients with frontotemporal lobar degeneration: a multicentre, randomised, double-blind, placebo-controlled trial. Lancet Neurol. 2013;12(2):149-156.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure.
Left Insula Fractional Amplitude of Low-frequency Fluctuation (fALFF) Scores and Symptom Change

A, Identification of left insula fALFF in patients having behavioral variant frontotemporal dementia (bvFTD) or semantic dementia vs healthy control subjects. B, Patient Frontal Behavioral Inventory (FBI) changes during 8 weeks and fALFF measures. The line represents the regression for the entire group (n = 15). The FBI change scores are residualized, controlling for baseline FBI scores. C, The FBI apathy subscore changes during 8 weeks and fALFF measures. D, The FBI disinhibition subscore changes and fALFF measures. Dashed vertical line indicates the mean fALFF activity measured in the control group.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 2.  Frontal Behavioral Inventory (FBI) Baseline Scores and Percentage Change at 8 Weeks for Individual Patients
Table Graphic Jump LocationTable 1.  Demographic and Clinical Characteristics of Study Patients

References

Farb  NA, Grady  CL, Strother  S,  et al.  Abnormal network connectivity in frontotemporal dementia: evidence for prefrontal isolation. Cortex. 2013;49(7):1856-1873.
PubMed   |  Link to Article
Zhou  J, Greicius  MD, Gennatas  ED,  et al.  Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer’s disease. Brain. 2010;133(pt 5):1352-1367.
PubMed   |  Link to Article
Seeley  WW, Crawford  RK, Zhou  J, Miller  BL, Greicius  MD.  Neurodegenerative diseases target large-scale human brain networks. Neuron. 2009;62(1):42-52.
PubMed   |  Link to Article
Brambati  SM, Rankin  KP, Narvid  J,  et al.  Atrophy progression in semantic dementia with asymmetric temporal involvement: a tensor-based morphometry study. Neurobiol Aging. 2009;30(1):103-111.
PubMed   |  Link to Article
Seeley  WW, Allman  JM, Carlin  DA,  et al.  Divergent social functioning in behavioral variant frontotemporal dementia and Alzheimer disease: reciprocal networks and neuronal evolution. Alzheimer Dis Assoc Disord. 2007;21(4):S50-S57.
PubMed   |  Link to Article
Kim  EJ, Sidhu  M, Gaus  SE,  et al.  Selective frontoinsular von Economo neuron and fork cell loss in early behavioral variant frontotemporal dementia. Cereb Cortex. 2012;22(2):251-259.
PubMed   |  Link to Article
Chow  TW, Links  KA, Masterman  DL, Mendez  MF, Vinters  HV.  A case of semantic variant primary progressive aphasia with severe insular atrophy. Neurocase. 2012;18(6):450-456.
PubMed   |  Link to Article
Neary  D, Snowden  JS, Gustafson  L,  et al.  Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology. 1998;51(6):1546-1554.
PubMed   |  Link to Article
Chow  TW, Graff-Guerrero  A, Verhoeff  NP,  et al.  Open-label study of the short-term effects of memantine on FDG-PET in frontotemporal dementia. Neuropsychiatr Dis Treat. 2011;7:415-424.
PubMed   |  Link to Article
Kertesz  A, Davidson  W, Fox  H.  Frontal Behavioral Inventory: diagnostic criteria for frontal lobe dementia. Can J Neurol Sci.1997;24(1):29-36.
Burke  WJ, Miller  JP, Rubin  EH,  et al.  Reliability of the Washington University Clinical Dementia Rating. Arch Neurol. 1988;45(1):31-32.
PubMed   |  Link to Article
Zou  QH, Zhu  CZ, Yang  Y,  et al.  An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods. 2008;172(1):137-141.
PubMed   |  Link to Article
Zang  Y, Jiang  T, Lu  Y, He  Y, Tian  L.  Regional homogeneity approach to fMRI data analysis. Neuroimage. 2004;22(1):394-400.
PubMed   |  Link to Article
Zou  Q, Wu  CW, Stein  EA, Zang  Y, Yang  Y.  Static and dynamic characteristics of cerebral blood flow during the resting state. Neuroimage. 2009;48(3):515-524.
PubMed   |  Link to Article
Chow  TW, Fridhandler  JD, Binns  MA,  et al.  Trajectories of behavioral disturbance in dementia. J Alzheimers Dis. 2012;31(1):143-149.
PubMed
Steigerwald  F, Pötter  M, Herzog  J,  et al.  Neuronal activity of the human subthalamic nucleus in the parkinsonian and nonparkinsonian state. J Neurophysiol. 2008;100(5):2515-2524.
PubMed   |  Link to Article
Seeley  WW, Crawford  RK, Rascovsky  K,  et al.  Frontal paralimbic network atrophy in very mild behavioral variant frontotemporal dementia. Arch Neurol. 2008;65(2):249-255.
PubMed   |  Link to Article
Boxer  AL, Knopman  DS, Kaufer  DI,  et al.  Memantine in patients with frontotemporal lobar degeneration: a multicentre, randomised, double-blind, placebo-controlled trial. Lancet Neurol. 2013;12(2):149-156.
PubMed   |  Link to Article

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Multimedia

Supplement.

eTable 1. Location of Salience Network Regions of Interest (ROIs) Identified Through the Contrast of FTD < Controls

eTable 2. Regression Model Details

eTable 3. Correlation Between Resting-State Predictor Variables, Control Variables, and Outcome Variables, Controlling for Baseline FBI Scores

eTable 4. Regression Model Details for the bvFTD Subgroup

eTable 5. Regression Model Details for the Semantic Dementia Subgroup

eTable 6. Comparison of Parametric and Nonparametric Correlations With Left Insula fALFF

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