Taxonomic Interference Associated with Phonemic Paraphasias in Agrammatic Primary Progressive Aphasia.

Phonemic paraphasias are thought to reflect phonological (post-semantic) deficits in language production. Here we present evidence that phonemic paraphasias in non-semantic primary progressive aphasia (PPA) may be associated with taxonomic interference. Agrammatic and logopenic PPA patients and control participants performed a word-to-picture visual search task where they matched a stimulus noun to 1 of 16 object pictures as their eye movements were recorded. Participants were subsequently asked to name the same items. We measured taxonomic interference (ratio of time spent viewing related vs. unrelated foils) during the search task for each item. Target items that elicited a phonemic paraphasia during object naming elicited increased taxonomic interference during the search task in agrammatic but not logopenic PPA patients. These results could reflect either very subtle sub-clinical semantic distortions of word representations or partial degradation of specific phonological word forms in agrammatic PPA during both word-to-picture matching (input stage) and picture naming (output stage). The mechanism for phonemic paraphasias in logopenic patients seems to be different and to be operative at the pre-articulatory stage of phonological retrieval. Glucose metabolic imaging suggests that degeneration in the left posterior frontal lobe and left temporo-parietal junction, respectively, might underlie these different patterns of phonemic paraphasia.

Validation of non-REM sleep stage decoding from resting state fMRI using linear support vector machines.

A growing body of literature suggests that changes in consciousness are reflected in specific connectivity patterns of the brain as obtained from resting state fMRI (rs-fMRI). As simultaneous electroencephalography (EEG) is often unavailable, decoding of potentially confounding sleep patterns from rs-fMRI itself might be useful and improve data interpretation. Linear support vector machine classifiers were trained on combined rs-fMRI/EEG recordings from 25 subjects to separate wakefulness (S0) from non-rapid eye movement (NREM) sleep stages 1 (S1), 2 (S2), slow wave sleep (SW) and all three sleep stages combined (SX). Classifier performance was quantified by a leave-one-subject-out cross-validation (LOSO-CV) and on an independent validation dataset comprising 19 subjects. Results demonstrated excellent performance with areas under the receiver operating characteristics curve (AUCs) close to 1.0 for the discrimination of sleep from wakefulness (S0|SX), S0|S1, S0|S2 and S0|SW, and good to excellent performance for the classification between sleep stages (S1|S2:~0.9; S1|SW:~1.0; S2|SW:~0.8). Application windows of fMRI data from about 70 s were found as minimum to provide reliable classifications. Discrimination patterns pointed to subcortical-cortical connectivity and within-occipital lobe reorganization of connectivity as strongest carriers of discriminative information. In conclusion, we report that functional connectivity analysis allows valid classification of NREM sleep stages.

Decoding subject-driven cognitive states with whole-brain connectivity patterns.

Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continuous and purely subject-driven cognitive states have not been available. Here, we demonstrate that free-streaming subject-driven cognitive states can be decoded using a novel whole-brain functional connectivity analysis. Ninety functional regions of interest (ROIs) were defined across 14 large-scale resting-state brain networks to generate a 3960 cell matrix reflecting whole-brain connectivity. We trained a classifier to identify specific patterns of whole-brain connectivity as subjects rested quietly, remembered the events of their day, subtracted numbers, or (silently) sang lyrics. In a leave-one-out cross-validation, the classifier identified these 4 cognitive states with 84% accuracy. More critically, the classifier achieved 85% accuracy when identifying these states in a second, independent cohort of subjects. Classification accuracy remained high with imaging runs as short as 30-60 s. At all temporal intervals assessed, the 90 functionally defined ROIs outperformed a set of 112 commonly used structural ROIs in classifying cognitive states. This approach should enable decoding a myriad of subject-driven cognitive states from brief imaging data samples.

Prospects for prediction: ethics analysis of neuroimaging in Alzheimer's disease.

This article focuses on the prospects and ethics of using neuroimaging to predict Alzheimer's disease (AD). It is motivated by consideration of the historical roles of science in medicine and society, and considerations specifically contemporary of capabilities in imaging and aging, and the benefits and hope they bring. A general consensus is that combinations of imaging methods will ultimately be most fruitful in predicting disease. Their roll-out into translational practice will not be free of complexity, however, as culture and values differ in terms of what defines benefit and risk, who will benefit and who is at risk, what methods must be in place to assure the maximum safety, comfort, and protection of subjects and patients, and educational and policy needs. Proactive planning for the ethical and societal implications of predicting diseases of the aging brain is critical and will benefit all stakeholders-researchers, patients and families, health care providers, and policy makers.

Comparison of fMRI activation at 3 and 1.5 T during perceptual, cognitive, and affective processing.

Previous studies comparing fMRI data acquired at 1.5 T and higher field strengths have focused on examining signal increases in the visual and motor cortices. No information is, however, available on the relative gain, or the comparability of data, obtained at higher field strengths for other brain regions such as the prefrontal and other association cortices. In the present study, we investigated fMRI activation at 1.5 and 3 T during visual perception, visuospatial working memory, and affect-processing tasks. A 23% increase in striate and extrastriate activation volume was observed at 3 T compared with that for 1.5 T during the visual perception task. During the working memory task significant increases in activation volume were observed in frontal and parietal association cortices as well as subcortical structures, including the caudate, globus pallidus, putamen, and thalamus. Increases in working memory-related activation volume of 82, 73, 83, and 36% were observed in the left frontal, right frontal, left parietal, and right parietal lobes, respectively, for 3 T compared with 1.5 T. These increases were characterized by increased activation at 3 T in several prefrontal and parietal cortex regions that showed activation at 1.5 T. More importantly, at 3 T, activation was detected in several regions, such as the ventral aspects of the inferior frontal gyrus, orbitofrontal gyrus, and lingual gyrus, which did not show significant activation at 1.5 T. No difference in height or extent of activation was detected between the two scanners in the amygdala during affect processing. Signal dropout in the amygdala from susceptibility artifact was greater at 3 T, with a 12% dropout at 3 T compared with a 9% dropout at 1.5 T. The spatial smoothness of T2* images was greater at 3 T by less than 1 mm, suggesting that the greater extent of activation at 3 T beyond these spatial scales was not due primarily to increased intrinsic spatial correlations at 3 T. Rather, the increase in percentage of voxels activated reflects increased sensitivity for detection of brain activation at higher field strength. In summary, our findings suggest that functional imaging of prefrontal and other association cortices can benefit significantly from higher magnetic field strength.

Presenile dementia syndromes: an update on taxonomy and diagnosis.

The four major degenerative dementias that often begin in presenescence: are reviewed. These are Alzheimer's disease, frontotemporal dementia, dementia with Lewy bodies, and Creutzfeldt-Jakob disease. Their epidemiological, genetic, and clinical features are reviewed, and controversies in taxonomy arising from recent discoveries described. Particular attention is given to the pathological role of protein aggregation, which appears to be a factor in each disease.