Functional identification of language-responsive channels in individual participants in MEG investigations.

Making meaningful inferences about the functional architecture of the language system requires the ability to refer to the same neural units across individuals and studies. Traditional brain imaging approaches align and average brains together in a common space. However, lateral frontal and temporal cortex, where the language system resides, is characterized by high structural and functional inter-individual variability. This variability reduces the sensitivity and functional resolution of group-averaging analyses. This problem is compounded by the fact that language areas often lay in close proximity to regions of other large-scale networks with different functional profiles. A solution inspired by other fields of cognitive neuroscience (e.g., vision) is to identify language areas functionally in each individual brain using a 'localizer' task (e.g., a language comprehension task). This approach has proven productive in fMRI, yielding a number of discoveries about the language system, and has been successfully extended to intracranial recording investigations. Here, we apply this approach to MEG. Across two experiments (one in Dutch speakers, n=19; one in English speakers, n=23), we examined neural responses to the processing of sentences and a control condition (nonword sequences). We demonstrated that the neural response to language is spatially consistent at the individual level. The language-responsive sensors of interest were, as expected, less responsive to the nonwords condition. Clear inter-individual differences were present in the topography of the neural response to language, leading to greater sensitivity when the data were analyzed at the individual level compared to the group level. Thus, as in fMRI, functional localization yields benefits in MEG and thus opens the door to probing fine-grained distinctions in space and time in future MEG investigations of language processing.

Probabilistic atlas for the language network based on precision fMRI data from >800 individuals.

Two analytic traditions characterize fMRI language research. One relies on averaging activations across individuals. This approach has limitations: because of inter-individual variability in the locations of language areas, any given voxel/vertex in a common brain space is part of the language network in some individuals but in others, may belong to a distinct network. An alternative approach relies on identifying language areas in each individual using a functional 'localizer'. Because of its greater sensitivity, functional resolution, and interpretability, functional localization is gaining popularity, but it is not always feasible, and cannot be applied retroactively to past studies. To bridge these disjoint approaches, we created a probabilistic functional atlas using fMRI data for an extensively validated language localizer in 806 individuals. This atlas enables estimating the probability that any given location in a common space belongs to the language network, and thus can help interpret group-level activation peaks and lesion locations, or select voxels/electrodes for analysis. More meaningful comparisons of findings across studies should increase robustness and replicability in language research.

Automated analysis of facial emotions in subjects with cognitive impairment.

Differences in expressing facial emotions are broadly observed in people with cognitive impairment. However, these differences have been difficult to objectively quantify and systematically evaluate among people with cognitive impairment across disease etiologies and severity. Therefore, a computer vision-based deep learning model for facial emotion recognition trained on 400.000 faces was utilized to analyze facial emotions expressed during a passive viewing memory test. In addition, this study was conducted on a large number of individuals (n = 493), including healthy controls and individuals with cognitive impairment due to diverse underlying etiologies and across different disease stages. Diagnoses included subjective cognitive impairment, Mild Cognitive Impairment (MCI) due to AD, MCI due to other etiologies, dementia due to Alzheimer's diseases (AD), and dementia due to other etiologies (e.g., Vascular Dementia, Frontotemporal Dementia, Lewy Body Dementia, etc.). The Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive performance across all participants. A participant with a score of less than or equal to 24 was considered cognitively impaired (CI). Compared to cognitively unimpaired (CU) participants, CI participants expressed significantly less positive emotions, more negative emotions, and higher facial expressiveness during the test. In addition, classification analysis revealed that facial emotions expressed during the test allowed effective differentiation of CI from CU participants, largely independent of sex, race, age, education level, mood, and eye movements (derived from an eye-tracking-based digital biomarker for cognitive impairment). No screening methods reliably differentiated the underlying etiology of the cognitive impairment. The findings provide quantitative and comprehensive evidence that the expression of facial emotions is significantly different in people with cognitive impairment, and suggests this may be a useful tool for passive screening of cognitive impairment.

Deep Convolutional Neural Networks and Transfer Learning for Measuring Cognitive Impairment Using Eye-Tracking in a Distributed Tablet-Based Environment.

OBJECTIVE: Alzheimer's disease (AD) is a neurodegenerative disorder that initially presents with memory loss in the presence of underlying neurofibrillary tangle and amyloid plaque pathology. Mild cognitive impairment is the initial symptomatic stage, which is an early window for detecting cognitive impairment prior to progressive decline and dementia. We recently developed the Visuospatial Memory Eye-Tracking Test (VisMET), a passive task capable of classifying cognitive impairment in AD in under five minutes. Here we describe the development of a mobile version of VisMET to enable efficient and widespread administration of the task. METHODS: We delivered VisMET on iPad devices and used a transfer learning approach to train a deep neural network to track eye gaze. Eye movements were used to extract memory features to assess cognitive status in a population of 250 individuals. RESULTS: Mild to severe cognitive impairment was identifiable with a test accuracy of 70%. By enforcing a minimal eye tracking calibration error of 2 cm, we achieved an accuracy of 76% which is equivalent to the accuracy obtained using commercial hardware for eye-tracking. CONCLUSION: This work demonstrates a mobile version of VisMET capable of estimating the presence of cognitive impairment. SIGNIFICANCE: Given the ubiquity of tablet devices, our approach has the potential to scale globally.

Speech-accompanying gestures are not processed by the language-processing mechanisms.

Speech-accompanying gestures constitute one information channel during communication. Some have argued that processing gestures engages the brain regions that support language comprehension. However, studies that have been used as evidence for shared mechanisms suffer from one or more of the following limitations: they (a) have not directly compared activations for gesture and language processing in the same study and relied on the fallacious reverse inference (Poldrack, 2006) for interpretation, (b) relied on traditional group analyses, which are bound to overestimate overlap (e.g., Nieto-Castañon and Fedorenko, 2012), (c) failed to directly compare the magnitudes of response (e.g., Chen et al., 2017), and (d) focused on gestures that may have activated the corresponding linguistic representations (e.g., "emblems"). To circumvent these limitations, we used fMRI to examine responses to gesture processing in language regions defined functionally in individual participants (e.g., Fedorenko et al., 2010), including directly comparing effect sizes, and covering a broad range of spontaneously generated co-speech gestures. Whenever speech was present, language regions responded robustly (and to a similar degree regardless of whether the video contained gestures or grooming movements). In contrast, and critically, responses in the language regions were low - at or slightly above the fixation baseline - when silent videos were processed (again, regardless of whether they contained gestures or grooming movements). Brain regions outside of the language network, including some in close proximity to its regions, differentiated between gestures and grooming movements, ruling out the possibility that the gesture/grooming manipulation was too subtle. Behavioral studies on the critical video materials further showed robust differentiation between the gesture and grooming conditions. In summary, contra prior claims, language-processing regions do not respond to co-speech gestures in the absence of speech, suggesting that these regions are selectively driven by linguistic input (e.g., Fedorenko et al., 2011). Although co-speech gestures are uncontroversially important in communication, they appear to be processed in brain regions distinct from those that support language comprehension, similar to other extra-linguistic communicative signals, like facial expressions and prosody.

Combination of stem cell and gene therapy ameliorates symptoms in Huntington's disease mice.

Huntington's disease (HD) is a dominantly inherited monogenetic disorder characterized by motor and cognitive dysfunction due to neurodegeneration. The disease is caused by the polyglutamine (polyQ) expansion at the 5' terminal of the exon 1 of the huntingtin (HTT) gene, IT15, which results in the accumulation of mutant HTT (mHTT) aggregates in neurons and cell death. The monogenetic cause and the loss of specific neural cell population make HD a suitable candidate for stem cell and gene therapy. In this study, we demonstrate the efficacy of the combination of stem cell and gene therapy in a transgenic HD mouse model (N171-82Q; HD mice) using rhesus monkey (Macaca mulatta) neural progenitor cells (NPCs). We have established monkey NPC cell lines from induced pluripotent stem cells (iPSCs) that can differentiate into GABAergic neurons in vitro as well as in mouse brains without tumor formation. Wild-type monkey NPCs (WT-NPCs), NPCs derived from a transgenic HD monkey (HD-NPCs), and genetically modified HD-NPCs with reduced mHTT levels by stable expression of small-hairpin RNA (HD-shHD-NPCs), were grafted into the striatum of WT and HD mice. Mice that received HD-shHD-NPC grafts showed a significant increase in lifespan compared to the sham injection group and HD mice. Both WT-NPC and HD-shHD-NPC grafts in HD mice showed significant improvement in motor functions assessed by rotarod and grip strength. Also, immunohistochemistry demonstrated the integration and differentiation. Our results suggest the combination of stem cell and gene therapy as a viable therapeutic option for HD treatment.

VisMET: a passive, efficient, and sensitive assessment of visuospatial memory in healthy aging, mild cognitive impairment, and Alzheimer's disease.

The entorhinal-hippocampal circuit is one of the earliest sites of cortical pathology in Alzheimer's disease (AD). Visuospatial memory paradigms that are mediated by the entorhinal-hippocampal circuit may offer a means to detect memory impairment during the early stages of AD. In this study, we developed a 4-min visuospatial memory paradigm called VisMET (Visuospatial Memory Eye-Tracking Task) that passively assesses memory using eye movements rather than explicit memory judgements. We had 296 control or memory-impaired participants view a set of images followed by a modified version of the images with either an object removed, or a new object added. Healthy controls spent significantly more time viewing these manipulations compared to subjects with mild cognitive impairment and AD. Using a logistic regression model, the amount of time that individuals viewed these manipulations could predict cognitive impairment and disease status with an out of sample area under the receiver-operator characteristic curve of 0.85. Based on these results, VisMET offers a passive, sensitive, and efficient memory paradigm capable of detecting objective memory impairment and predicting cognitive and disease status.

Longitudinal Anthropometric Assessment of Rhesus Macaque (Macaca mulatta) Model of Huntington Disease.

The neurodegeneration associated with Huntington disease (HD) leads to the onset of motor and cognitive impairment and their advancement with increased age in humans. In children at risk for HD, body measurement growth abnormalities include a reduction in BMI, weight, height, and head circumference. The transgenic HD NHP model was first reported in 2008, and progressive decline in cognitive behaviors and motor impairment have been reported. This study focuses on longitudinal body measurements in HD macaques from infancy through adulthood. The growth of HD macaques was assessed through head circumference, sagittal and transverse head, and crown-to-rump ('height') measurements and BMI. The animals were measured monthly from 0 to 72 mo of age and every 3 mo from 72 mo of age onward. A mixed-effect model was used to assess subject-specific effects in our nonlinear serial data. Compared with WT controls, HD macaques displayed different developmental trajectories characterized by increased BMI, head circumference, and sagittal head measurements beginning around 40 mo of age. The physiologic comparability between NHP and humans underscores the translational utility of our HD macaques to evaluate growth and developmental patterns associated with HD.