Frontotemporal Dementia: Correlations Between Psychiatric Symptoms and Pathology.

OBJECTIVE: The pathology of frontotemporal dementia, termed frontotemporal lobar degeneration (FTLD), is characterized by distinct molecular classes of aggregated proteins, the most common being TAR DNA-binding protein-43 (TDP-43), tau, and fused in sarcoma (FUS). With a few exceptions, it is currently not possible to predict the underlying pathology based on the clinical syndrome. In this study, we set out to investigate the relationship between pathological and clinical presentation at single symptom level, including neuropsychiatric features. METHODS: The presence or absence of symptoms from the current clinical guidelines, together with neuropsychiatric features, such as hallucinations and delusions, were scored and compared across pathological groups in a cohort of 150 brain donors. RESULTS: Our cohort consisted of 68.6% FTLD donors (35.3% TDP-43, 28% tau, and 5.3% FUS) and 31.3% non-FTLD donors with a clinical diagnosis of frontotemporal dementia and a different pathological substrate, such as Alzheimer's disease (23%). The presence of hyperorality points to FTLD rather than non-FTLD pathology (p < 0.001). Within the FTLD group, hallucinations in the initial years of the disease were related to TDP-43 pathology (p = 0.02), including but not limited to chromosome 9 open reading frame 72 (C9orf72) repeat expansion carriers. The presence of perseverative or compulsive behavior was more common in the TDP-B and TDP-C histotypes (p = 0.002). INTERPRETATION: Our findings indicate that neuropsychiatric features are common in FTLD and form an important indicator of underlying pathology. In order to allow better inclusion of patients in targeted molecular trials, the routine evaluation of patients with frontotemporal dementia should include the presence and nature of neuropsychiatric symptoms. ANN NEUROL 2020;87:950-961.

State Changes During Resting-State (Magneto)encephalographic Studies: The Effect of Drowsiness on Spectral, Connectivity, and Network Analyses.

Background: A common problem in resting-state neuroimaging studies is that subjects become drowsy or fall asleep. Although this could drastically affect neurophysiological measurements, such as magnetoencephalography (MEG), its specific impact remains understudied. We aimed to systematically investigate how often drowsiness is present during resting-state MEG recordings, and how the state changes alter quantitative estimates of oscillatory activity, functional connectivity, and network topology. Methods: About 8-min MEG recordings of 19 healthy subjects, split into ~13-s epochs, were scored for the presence of eyes-open (EO), alert eyes-closed (A-EC), or drowsy eyes-closed (D-EC) states. After projection to source-space, results of spectral, functional connectivity, and network analyses in 6 canonical frequency bands were compared between these states on a global and regional levels. Functional connectivity was analyzed using the phase lag index (PLI) and corrected amplitude envelope correlation (AECc), and network topology was analyzed using the minimum spanning tree (MST). Results: Drowsiness was present in >55% of all epochs that did not fulfill the AASM criteria for sleep. There were clear differences in spectral results between the states (A-EC vs. D-EC) and conditions (EO vs. A-EC). The influence of state and condition was far less pronounced for connectivity analyses, with only minimal differences between D-EC and EO in the AECc in the delta band. There were no effects of drowsiness on any of the MST measures. Conclusions: Drowsiness during eyes-closed resting-state MEG recordings is present in the majority of epochs, despite the instructions to stay awake. This has considerable influence on spectral properties, but much less so on functional connectivity and network topology. These findings are important for interpreting the results of EEG/MEG studies using spectral analyses in neurological disease, where recordings should be evaluated for the presence of drowsiness. For connectivity analyses or studies on network topology, this seems of far less importance.