These findings provide evidence that large-scale networks are required for fluent, grammatical expression; that these networks can be selectively disrupted in PPA syndromes; and that quantitative analysis of a brief speech sample can reveal the corresponding distinct speech characteristics.

Background Significant heterogeneity in clinical features of frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS) cases with the pathogenic C9orf72 expansion (C9P) have been described. To clarify this issue, we compared a large C9P cohort with carefully matched non-expansion (C9N) cases with a known or highly-suspected underlying TDP-43 proteinopathy. Methods A retrospective-cohort study using available cross-sectional and longitudinal clinical and neuropsychological data, MRI voxel-based morphometry (VBM) and neuropathological assessment from 64 C9P cases (ALS=31, FTLD=33) and 79 C9N cases (ALS=36, FTLD=43). Results C9P cases had an earlier age of onset (p=0.047), and in the subset of deceased patients, an earlier age of death (p=0.014) than C9N. C9P had more rapid progression than C9N: C9P ALS cases had a shortened survival (2.6±0.3 years) compared to C9N ALS (3.8±0.4 years; log-rankλ2=4.183,p=0.041), and C9P FTLD showed a significantly greater annualized rate of decline in letter fluency (4.5±1.3words/year) than C9N FTLD (1.4±0.8words/year, p=0.023). VBM revealed greater atrophy in the right fronto-insular, thalamus, cerebellum and bilateral parietal regions for C9P FTLD relative to C9N FTLD, and regression analysis related verbal fluency scores to atrophy in frontal and parietal regions. Neuropathologic analysis found greater neuronal loss in the mid-frontal cortex in C9P FTLD, and mid-frontal cortex TDP-43 inclusion severity correlated with poor letter fluency performance. Conclusions C9P cases may have a shorter survival in ALS and more rapid rate of cognitive decline related to frontal and parietal disease in FTLD. C9orf72 genotyping may provide useful prognostic and diagnostic clinical information for ALS and FTLD patients.

Distancing is a type of emotion regulation that involves simulating a new perspective to alter the psychological distance and emotional impact of a stimulus. The effectiveness and versatility of distancing relative to other types of emotion regulation make it a promising tool for clinical applications. However, the neurocognitive mechanisms of this tactic are unclear, and inconsistencies in terminology and methods across studies make it difficult to synthesize the literature. To promote more effective research, we propose a taxonomy of distancing within the broader context of emotion regulation strategies; review the effects of this tactic; and offer a preliminary neurocognitive model describing key cognitive processes and their neural bases. Our model emphasizes three components—self-projection, affective self-reflection, and cognitive control. Additionally, we present results from a supporting meta-analysis of neuroimaging studies of distancing. These efforts are presented within the overarching goals of supporting effective applications of distancing in laboratory, clinical, and other real-world contexts, and advancing understanding of the relevant high-level cognitive functions in the brain.

The brain is organized into networks that reorganize dynamically in response to cognitive demands and exogenous stimuli. In recent years, repetitive transcranial magnetic stimulation (rTMS) has gained increasing use as a noninvasive means to modulate cortical physiology, with effects both proximal to the stimulation site and in distal areas that are intrinsically connected to the proximal target. In light of these network-level neuromodulatory effects, there has been a rapid growth in studies attempting to leverage information about network connectivity to improve neuromodulatory control and intervention outcomes. However, the mechanisms-of-action of rTMS on network-level effects remain poorly understood and is based primarily on heuristics from proximal stimulation findings. To help bridge this gap, the current paper presents a systematic review of 33 rTMS studies with baseline and post-rTMS measures of fMRI resting-state functional connectivity (RSFC). Literature synthesis revealed variability across studies in stimulation parameters, studied populations, and connectivity analysis methodology. Despite this variability, it is observed that active rTMS induces significant changes on RSFC, but the prevalent low-frequency-inhibition/high-frequency-facilitation heuristic endorsed for proximal rTMS effects does not fully describe distal connectivity findings. This review also points towards other important considerations, including that the majority of rTMS-induced changes were found outside the stimulated functional network, suggesting that rTMS effects tend to spread across networks. Future studies may therefore wish to adopt conventions and systematic frameworks, such as the Yeo functional connectivity parcellation atlas adopted here, to better characterize network-level effect that contribute to the efficacy of these rapidly developing noninvasive interventions.

Non-fluent/agrammatic primary progressive aphasia (naPPA) is a progressive neurodegenerative condition most prominently associated with slowed, effortful speech. A clinical imaging marker of naPPA is disease centered in the left inferior frontal lobe. We used multimodal imaging to assess large-scale neural networks underlying effortful expression in 15 patients with sporadic naPPA due to frontotemporal lobar degeneration (FTLD) spectrum pathology. Effortful speech in these patients is related in part to impaired grammatical processing, and to phonologic speech errors. Gray matter (GM) imaging shows frontal and anterior-superior temporal atrophy, most prominently in the left hemisphere. Diffusion tensor imaging reveals reduced fractional anisotropy in several white matter (WM) tracts mediating projections between left frontal and other GM regions. Regression analyses suggest disruption of three large-scale GM-WM neural networks in naPPA that support fluent, grammatical expression. These findings emphasize the role of large-scale neural networks in language, and demonstrate associated language deficits in naPPA.