Based on the Network Degeneration Hypothesis: Separating Individual Patients with Different Neurodegenerative Syndromes in a Preliminary Hybrid PET/MR Study

Abstract

The network degeneration hypothesis (NDH) of neurodegenerative syndromes suggests that pathologic brain changes distribute primarily along distinct brain networks, which are characteristic for different syndromes. Brain changes of neurodegenerative syndromes can be characterized in vivo by different imaging modalities. Our aim was to test the hypothesis whether multimodal imaging based on the NDH separates individual patients with different neurodegenerative syndromes. Methods: Twenty patients with Alzheimer disease (AD) and 20 patients with frontotemporal lobar degeneration (behavioral variant frontotemporal dementia [bvFTD, n 5 11], semantic dementia [SD, n 5 4], or progressive nonfluent aphasia [PNFA, n 5 5]) underwent simultaneous MRI and 18 F-FDG PET in a hybrid PET/MR scanner. The 3 outcome measures were voxelwise values of degree centrality as a surrogate for regional functional connectivity, glucose metabolism as a surrogate for regional metabolism, and volumetric-based morphometry as a surrogate for regional gray matter volume. Outcome measures were derived from predefined core regions of 4 intrinsic networks based on the NDH, which have been demonstrated to be characteristic for AD, bvFTD, SD, and PNFA, respectively. Subsequently, we applied support vector machine to classify individual patients via combined imaging measures, and results were evaluated by leave-one-out cross-validation. Results: On the basis of multimodal voxelwise regional patterns, classification accuracies for separating patients with different neurodegenerative syndromes were 77.5% for AD versus others, 82.5% for bvFTD versus others, 97.5% for SD versus others, and 87.5% for PNFA versus others. Multimodal classification results were significantly superior to unimodal approaches. Conclusion: Our finding provides initial evidence that the combination of regional metabolism, functional connectivity, and gray matter volume, which were derived from disease characteristic networks, separates individual patients with different neurodegenerative syndromes. Preliminary results suggest that employing multimodal imaging guided by the NDH may generate promising biomarkers of neurodegenerative syndromes.

Structural and functional neural adaptations in obstructive sleep apnea: An activation likelihood estimation meta-analysis

“…Hence, it follows that the importance of hypotrophy of such pivotal region such is basolateral nucleus of amygdala in patients with OSA should not be ignored. It is easy to postulate that this deficit must have reverberating impact on the patients’ ability to decipher the very valence of ongoing life experiences and that in others lead to emotional blunting such can be seen in Alzheimer’s disease (AD) or behavioural variant frontotemporal dementia (Rosen et al, 2002, Tahmasian et al, 2015b, Tahmasian et al, 2016). …”

“…The function and structure of such networks is of particular interest given that abnormalities in the interactions of network components can play a critical role in neuropsychiatric disorders, with damage to specific functional connectivity nodes and networks giving rise to distinct neurological and psychiatric syndromes (Bassett and Bullmore, 2009, Seeley et al, 2009, Tahmasian et al, 2016, Tahmasian et al, 2015a, Tahmasian et al, 2015b, Tahmasian et al, 2013). The results of our conjunctional meta-analysis point to functionally important co-activation between our seeds (in the right amygdala/hippocampus and right central insula) and the bilateral anterior insula, bilateral thalamus, as well as the posterior-medial frontal cortex (Fig.…”

Prospective Validation of 18F-FDG Brain PET Discriminant Analysis Methods in the Diagnosis of Amyotrophic Lateral Sclerosis

“…On the basis of the Alzheimer Disease Neuroimaging Initiative database, the combination of 18 F-FDG PET and volumetric MR resulted in an accuracy of 91% to classify AD patients from controls (26). In the differential diagnosis of dementing disorders, Tahmasian et al studied the ability of combined 18 F-FDG PET and MR with SVM and found accuracies of 77.5% for AD, 82.5% for frontotemporal dementia, 97.5% for semantic dementia, and 87.5% for progressive nonfluent aphasia (27). The magnitude of accuracies in this setting using either 18 F-FDG or 18 F-FDG plus MR information is in line with our current results for ALS and provide an avenue to optimize the diagnostic performance of 18 F-FDG PET.…”

Behavioural and neuroimaging correlates of impaired self-awareness of hypo- and hyperkinesia in Parkinson's disease

“…The Wellcome Department of Imaging Neuroscience, London, UK) as described previously (Eggers, Schwartz, Pedrosa, Kracht, & Timmermann, 2014;Klupp et al, 2015;Tahmasian, Pasquini, et al, 2015;Tahmasian, Rochhausen, et al, 2015;Tahmasian, Shao, et al, 2015).…”