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Igor Yakushev

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12 papers

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YNIMG Journal 2025 Journal Article

Test-retest reproducibility of structural and proxy estimates of brain connectivity at rest

Aldana Lizarraga
Arianna Sala
Kathrin Koch
Igor Yakushev

While structural connectivity (SC) reflects actual neural connections, proxy estimates of brain connectivity such as functional connectivity (FC) from functional MRI, intersubject covariance of regional gray matter volume (GMVcov) from structural MRI, and intersubject covariance of regional 18F-fluorodeoxyglucose uptake (FDGcov) from PET are derived from statistical dependencies between regional measurements. Understanding reproducibility of these estimates is crucial for capturing physiological and pathological changes in brain connectivity. Here, we compared test-retest reproducibility of group-level SC, FC, GMVcov, and FDGcov in the same 55 healthy subjects at rest using a simultaneous PET/MRI acquisition protocol. Sparsity-based thresholding resulted in coefficients of variation of 2. 7 %, 3. 1 %, 3. 6 %, and 5. 1 % for SC, FDGcov, GMVcov, and FC, respectively. Strength-based thresholding resulted in relative proportion of repeatedly (test-retest) present connections (PRPC) of 77 %, 56 %, and 54 % for GMVcov, FDGcov, and FC, respectively. Absolute PRPC were 2. 50 %, 0. 64 %, and 0. 25 % for FDGcov, FC, and GMVcov, respectively. Overall, stronger connections exhibited higher reproducibility, arguing in favour of thresholding as a necessary analytical step. SC was most reproducible. Among the proxy estimates, the highest absolute PRPC was found for FDGcov. FDGcov enables to study brain connectivity in a reproducible manner over the largest part of the brain.

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YNIMG Journal 2022 Journal Article

Covariance-based vs. correlation-based functional connectivity dissociates healthy aging from Alzheimer disease

Jeremy F. Strain
Matthew R. Brier
Aaron Tanenbaum
Brian A. Gordon
John E. McCarthy
Aylin Dincer
Daniel S. Marcus
Jasmeer P. Chhatwal

Prior studies of aging and Alzheimer disease have evaluated resting state functional connectivity (FC) using either seed-based correlation (SBC) or independent component analysis (ICA), with a focus on particular functional systems. SBC and ICA both are insensitive to differences in signal amplitude. At the same time, accumulating evidence indicates that the amplitude of spontaneous BOLD signal fluctuations is physiologically meaningful. We systematically compared covariance-based FC, which is sensitive to amplitude, vs. correlation-based FC, which is not, in affected individuals and controls drawn from two cohorts of participants including autosomal dominant Alzheimer disease (ADAD), late onset Alzheimer disease (LOAD), and age-matched controls. Functional connectivity was computed over 222 regions of interest and group differences were evaluated in terms of components projected onto a space of lower dimension. Our principal observations are: (1) Aging is associated with global loss of resting state fMRI signal amplitude that is approximately uniform across resting state networks. (2) Thus, covariance FC measures decrease with age whereas correlation FC is relatively preserved in healthy aging. (3) In contrast, symptomatic ADAD and LOAD both lead to loss of spontaneous activity amplitude as well as severely degraded correlation structure. These results demonstrate a double dissociation between age vs. Alzheimer disease and the amplitude vs. correlation structure of resting state BOLD signals. Modeling results suggest that the AD-associated loss of correlation structure is attributable to a relative increase in the fraction of locally restricted as opposed to widely shared variance.

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YNIMG Journal 2022 Journal Article

Neuronal efficiency following n-back training task is accompanied by a higher cerebral glucose metabolism

Isabelle Ripp
Qiong Wu
Lara Wallenwein
Mónica Emch
Igor Yakushev
Kathrin Koch

Recent functional magnetic resonance imaging (fMRI) studies revealed lower neural activation during processing of an n-back task following working memory training, indicating a training-related increase in neural efficiency. In the present study, we asked if the training induced regional neural activation is accompanied by changes in glucose consumption. An active control and an experimental group of healthy middle-aged volunteers conducted 32 sessions of visual and verbal n-back trainings over 8 weeks. We analyzed data of 52 subjects (25 experimental and 27 control group) for practice effects underlying verbal working memory task and 50 subjects (24 experimental and 26 control group) for practice effects underlying visual WM task. The samples of these two tasks were nearly identical (data of 47 subjects were available for both verbal and visual tasks). Both groups completed neuroimaging sessions at a hybrid PET/MR system before and after training. Each session included criterion task fMRI and resting state positron emission tomography with FDG (FDG-PET). As reported previously, lower neural activation following n-back training was found in regions of the fronto-parieto-cerebellar circuitry during a verbal n-back task. Notably, these changes co-occurred spatially with a higher relative FDG-uptake. Decreased neural activation within regions of the fronto-parietal network during visual n-back task did not show co-occurring changes in relative FDG-uptake. There was no direct association between neuroimaging and behavioral measures, which could be due to the inter-subjects' variability in reaching capacity limits. Our findings provide new details for working memory training induced neural efficiency on a molecular level by integrating FDG-PET and fMRI measures.

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YNIMG Journal 2022 Journal Article

Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease

Peter R. Millar
Patrick H. Luckett
Brian A. Gordon
Tammie L.S. Benzinger
Suzanne E. Schindler
Anne M. Fagan
Randall J. Bateman
Jae-Hong Lee

"Brain-predicted age" quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18-89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.

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YNIMG Journal 2021 Journal Article

Working memory task induced neural activation: A simultaneous PET/fMRI study

Isabelle Ripp
Lara A Wallenwein
Qiong Wu
Monica Emch
Kathrin Koch
Paul Cumming
Igor Yakushev

PURPOSE: F]fluorodeoxyglucose (FDG) is a powerful method for mapping cerebral glucose metabolism as a proxy of neural activity, assuming a steady-state during the recording interval. We asked if a clinical FDG-PET imaging protocol might also capture changes in neural activity associated with performance of a working memory (WM) task. METHODS: To test this concept, we examined hybrid PET/MR data for FDG-PET and simultaneous functional magnetic resonance imaging (fMRI) in a sample of healthy volunteers. The PET image acquisition started 30 min after a bolus injection of approximately 100 MBq FDG, and the WM task was undertaken starting at approximately 60 min post-injection. We reconstructed FDG-PET sum images corresponding to baseline (44-60 min p.i.) and WM tasks (63- 71 min p.i.), each with intensity scaling to the corresponding global mean. RESULTS: Compared to the baseline resting condition, relative FDG uptake increased during WM task performance in brain regions previously associated with WM. Furthermore, these metabolically active regions partly overlapped with the regions showing task-dependent increases in BOLD signal in simultaneous fMRI. CONCLUSION: We find evidence for WM task-induced neural activation using a clinical FDG-PET imaging protocol. These findings encourage the development of dedicated protocols for tracking neural correlates of cognitive function.

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YNICL Journal 2020 Journal Article

Characterizing the heterogeneous metabolic progression in idiopathic REM sleep behavior disorder

Xianhua Han
Ping Wu
Ian Alberts
Hucheng Zhou
Huan Yu
Panagiotis Bargiotas
Igor Yakushev
Jian Wang

OBJECTIVE: F-FDG reveals metabolic perturbations, which are scored by spatial covariance analysis. However, the resultant pattern scores do not capture the spatially heterogeneous trajectories of metabolic changes between individual brain regions. Assuming metabolic progression occurs as a continuum from the healthy control (HC) condition to iRBD and then PD, we investigated spatial dynamics of progressively perturbed glucose metabolism in a cross-sectional study. METHODS: F-FDG uptake and the Unified Parkinson's Disease Rating Scale motor (UPDRS III) scores in the PD group. RESULTS: F-FDG metabolism and disease duration in the iRBD group. Regional hyper- and hypo-metabolism in the PD patients correlated with disease duration or clinical UPDRS III scores. CONCLUSION: Cerebral metabolism changes heterogeneously in a continuum extending from HC to iRBD and PD groups in this preliminary study. The distinctive metabolic trajectories point towards a potential neuroimaging biomarker for conversion of iRBD to frank PD, which should be amenable to advanced pattern recognition analysis in future longitudinal studies.

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YNICL Journal 2020 Journal Article

Comparing cortical signatures of atrophy between late-onset and autosomal dominant Alzheimer disease

Aylin Dincer
Brian A. Gordon
Amrita Hari-Raj
Sarah J. Keefe
Shaney Flores
Nicole S. McKay
Angela M. Paulick
Kristine E. Shady Lewis

F-florbetapir. To generate cortical signature maps of cortical thickness, we performed a vertex-wise analysis between the cognitively normal controls and impaired groups within each cohort using six increasingly conservative statistical thresholds to determine significance. The optimal cortical map among the six statistical thresholds was determined from a receiver operating characteristic analysis testing the performance of each map in discriminating between the cognitively normal controls and preclinical groups. We then performed within-cohort and cross-cohort (e.g. ADAD maps evaluated in the Knight ADRC cohort) analyses to examine the sensitivity of the optimal cortical signature maps to the amyloid levels using only the cognitively normal individuals (cognitively normal controls and preclinical groups) in comparison to hippocampal volume. We found the optimal cortical signature maps were sensitive to early increases in amyloid for the asymptomatic individuals within their respective cohorts and were significant beyond the inclusion of hippocampus volume, but the cortical signature maps performed poorly when analyzing across cohorts. These results suggest the cortical signature maps are a useful MRI biomarker of early AD-related neurodegeneration in preclinical individuals and the pattern of decline differs between LOAD and ADAD.

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YNICL Journal 2017 Journal Article

Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data

Sebastian Meyer
Karsten Mueller
Katharina Stuke
Sandrine Bisenius
Janine Diehl-Schmid
Frank Jessen
Jan Kassubek
Johannes Kornhuber

PURPOSE: Frontotemporal lobar degeneration (FTLD) is a common cause of early onset dementia. Behavioral variant frontotemporal dementia (bvFTD), its most common subtype, is characterized by deep alterations in behavior and personality. In 2011, new diagnostic criteria were suggested that incorporate imaging criteria into diagnostic algorithms. The study aimed at validating the potential of imaging criteria to individually predict diagnosis with machine learning algorithms. MATERIALS & METHODS: Brain atrophy was measured with structural magnetic resonance imaging (MRI) at 3 Tesla in a multi-centric cohort of 52 bvFTD patients and 52 healthy control subjects from the German FTLD Consortium's Study. Beside group comparisons, diagnosis bvFTD vs. controls was individually predicted in each subject with support vector machine classification in MRI data across the whole brain or in frontotemporal, insular regions, and basal ganglia known to be mainly affected based on recent meta-analyses. Multi-center effects were controlled for with a new method, "leave one center out" conjunction analyses, i.e. repeatedly excluding subjects from each center from the analysis. RESULTS: Group comparisons revealed atrophy in, most consistently, the frontal lobe in bvFTD beside alterations in the insula, basal ganglia and temporal lobe. Most remarkably, support vector machine classification enabled predicting diagnosis in single patients with a high accuracy of up to 84.6%, where accuracy was highest in a region-of-interest approach focusing on frontotemporal, insular regions, and basal ganglia in comparison with the whole brain approach. CONCLUSION: Our study demonstrates that MRI, a widespread imaging technology, can individually identify bvFTD with high accuracy in multi-center imaging data, paving the road to personalized diagnostic approaches in the future.

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YNIMG Journal 2013 Journal Article

Metabolic and structural connectivity within the default mode network relates to working memory performance in young healthy adults

Igor Yakushev
Gael Chételat
Florian U. Fischer
Brigitte Landeau
Christine Bastin
Armin Scheurich
Audrey Perrotin
Mohamed Ali Bahri

Studies of functional connectivity suggest that the default mode network (DMN) might be relevant for cognitive functions. Here, we examined metabolic and structural connectivity between major DMN nodes, the posterior cingulate (PCC) and medial prefrontal cortex (MPFC), in relation to normal working memory (WM). DMN was captured using independent component analysis of [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) data from 35 young healthy adults (27. 1±5. 1years). Metabolic connectivity, a correlation between FDG uptake in PCC and MPFC, was examined in groups of subjects with (relative to median) low (n=18) and high (n=17) performance on digit span backward test as an index of verbal WM. In addition, fiber tractography based on PCC and MPFC nodes as way points was performed in a subset of subjects. FDG uptake in the DMN nodes did not differ between high and low performers. However, significantly (p=0. 01) lower metabolic connectivity was found in the group of low performers. Furthermore, as compared to high performers, low performers showed lower density of the left superior cingulate bundle. Verbal WM performance is related to metabolic and structural connectivity within the DMN in young healthy adults. Metabolic connectivity as quantified with FDG-PET might be a sensitive marker of the normal variability in some cognitive functions.

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YNIMG Journal 2013 Journal Article

Relationships between brain metabolism decrease in normal aging and changes in structural and functional connectivity

Gaël Chételat
Brigitte Landeau
Eric Salmon
Igor Yakushev
Mohamed Ali Bahri
Florence Mézenge
Audrey Perrotin
Christine Bastin

Normal aging is characterized by brain glucose metabolism decline predominantly in the prefrontal cortex. The goal of the present study was to assess whether this change was associated with age-related alteration of white matter (WM) structural integrity and/or functional connectivity. FDG-PET data from 40 young and 57 elderly healthy participants from two research centers (n=49/48 in Center 1/2) were analyzed. WM volume from T1-weighted MRI (Center 1), fractional anisotropy from diffusion-tensor imaging (Center 2), and resting-state fMRI data (Center 1) were also obtained. Group comparisons were performed within each imaging modality. Then, positive correlations were assessed, within the elderly, between metabolism in the most affected region and the other neuroimaging modalities. Metabolism decline in the elderly predominated in the left inferior frontal junction (LIFJ). LIFJ hypometabolism was significantly associated with macrostructural and microstructural WM disturbances in long association fronto-temporo-occipital fibers, while no relationship was found with functional connectivity. The findings offer new perspectives to understand normal aging processes and open avenues for future studies to explore causality between age-related metabolism and connectivity changes.

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YNIMG Journal 2012 Journal Article

Cognitive reserve impacts on inter-individual variability in resting-state cerebral metabolism in normal aging

Christine Bastin
Igor Yakushev
Mohamed Ali Bahri
Andreas Fellgiebel
Francis Eustache
Brigitte Landeau
Armin Scheurich
Dorothée Feyers

There is a great deal of heterogeneity in the impact of aging on cognition and cerebral functioning. One potential factor contributing to individual differences among the elderly is the cognitive reserve, which designates the partial protection from the deleterious effects of aging that lifetime experience provides. Neuroimaging studies examining task-related activation in elderly people suggested that cognitive reserve takes the form of more efficient use of brain networks and/or greater ability to recruit alternative networks to compensate for age-related cerebral changes. In this exploratory multi-center study, we examined the relationships between cognitive reserve, as measured by education and verbal intelligence, and cerebral metabolism at rest (FDG-PET) in a sample of 74 healthy older participants. Higher degree of education and verbal intelligence was associated with less metabolic activity in the right posterior temporoparietal cortex and the left anterior intraparietal sulcus. Functional connectivity analyses of resting-state fMRI images in a subset of 41 participants indicated that these regions belong to the default mode network and the dorsal attention network respectively. Lower metabolism in the temporoparietal cortex was also associated with better memory abilities. The findings provide evidence for an inverse relationship between cognitive reserve and resting-state activity in key regions of two functional networks respectively involved in internal mentation and goal-directed attention.

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YNIMG Journal 2009 Journal Article

SPM-based count normalization provides excellent discrimination of mild Alzheimer's disease and amnestic mild cognitive impairment from healthy aging

Igor Yakushev
Alexander Hammers
Andreas Fellgiebel
Irene Schmidtmann
Armin Scheurich
Hans-Georg Buchholz
Jürgen Peters
Peter Bartenstein

Statistical comparisons of [18F]FDG PET scans between healthy subjects and patients with Alzheimer's disease (AD) or amnestic mild cognitive impairment (aMCI) using Statistical Parametric Mapping (SPM) usually require normalization of regional tracer uptake via ROIs defined using additional software. Here, we validate a simple SPM-based method for count normalization. FDG PET scans of 21 mild, 15 very mild AD, 11 aMCI patients and 15 age-matched controls were analyzed. First, we obtained relative increases in the whole patient sample compared to controls (i. e. areas relatively preserved in patients) with proportional scaling to the cerebral global mean (CGM). Next, average absolute counts within the cluster with the highest t-value were extracted. Statistical comparisons of controls versus three patients groups were then performed using count normalization to CGM, sensorimotor cortex (SMC) as standard, and to the cluster-derived counts. Compared to controls, relative metabolism in aMCI patients was reduced by 15%, 20%, and 23% after normalization to CGM, SMC, and cluster-derived counts, respectively, and 11%, 21%, and 25% in mild AD patients. Logistic regression analyses based on normalized values extracted from AD-typical regions showed that the metabolic values obtained using CGM, SMC, and cluster normalization correctly classified 81%, 89% and 92% of aMCI and controls; classification accuracies for AD groups (very mild and mild) were 91%, 97%, and 100%. The proposed algorithm of fully SPM-based count normalization allows for a substantial increase of statistical power in detecting very early AD-associated hypometabolism, and very high accuracy in discriminating mild AD and aMCI from healthy aging.

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