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Background and Purpose- Cerebral small vessel disease is characterized by a wide range of focal and global brain changes. We used a magnetic resonance imaging segmentation tool to quantify multiple types of small vessel disease-related brain changes and examined their individual and combined predictive value on cognitive and functional abilities. Methods- Magnetic resonance imaging scans of 560 older individuals from LADIS (Leukoaraiosis and Disability Study) were analyzed using automated atlas- and convolutional neural network-based segmentation methods yielding volumetric measures of white matter hyperintensities, lacunes, enlarged perivascular spaces, chronic cortical infarcts, and global and regional brain atrophy. The subjects were followed up with annual neuropsychological examinations for 3 years and evaluation of instrumental activities of daily living for 7 years. Results- The strongest predictors of cognitive performance and functional outcome over time were the total volumes of white matter hyperintensities, gray matter, and hippocampi ( P <0.001 for global cognitive function, processing speed, executive functions, and memory and P <0.001 for poor functional outcome). Volumes of lacunes, enlarged perivascular spaces, and cortical infarcts were significantly associated with part of the outcome measures, but their contribution was weaker. In a multivariable linear mixed model, volumes of white matter hyperintensities, lacunes, gray matter, and hippocampi remained as independent predictors of cognitive impairment. A combined measure of these markers based on Z scores strongly predicted cognitive and functional outcomes ( P <0.001) even above the contribution of the individual brain changes. Conclusions- Global burden of small vessel disease-related brain changes as quantified by an image segmentation tool is a powerful predictor of long-term cognitive decline and functional disability. A combined measure of white matter hyperintensities, lacunar, gray matter, and hippocampal volumes could be used as an imaging marker associated with vascular cognitive impairment.

Goal-directed behavior lowers activity in brain areas that include the medial frontal cortex, the medial and lateral parietal cortex, and limbic and paralimbic brain regions, commonly referred to as the "default network." These activity decreases are believed to reflect the interruption of processes that are ongoing when the mind is in a restful state. Previously, the nature of these processes was probed by varying cognitive task parameters, but the presence of emotional processes, while often assumed, was little investigated. With fMRI, we studied the effect of systematic variations of both cognitive load and emotional stimulus connotation on task-related decreases in the default network by employing an auditory working memory (WM) task with musical sounds. The performance of the WM task, compared to passive listening, lowered the activity in medial and lateral, prefrontal, parietal, temporal, and limbic regions. In a subset of these regions, the magnitude of decrease depended on the memory load; the greater the cognitive load, the larger the magnitude of the observed decrease. Furthermore, in the right amygdala and the left precuneus, areas previously associated with processing of unpleasant dissonant musical sounds, there was an interaction between the experimental condition and the stimulus type. The current results are consistent with the previously reported effect of task difficulty on task-related brain activation decreases. The results also indicate that task-related decreases may be further modulated by the emotional stimulus connotation.

Using functional magnetic resonance imaging (fMRI) and n-back tasks we investigated whether, in 11-13-year-old children, spatial (location) and nonspatial (color) information is differentially processed during visual attention (0-back) and working memory (WM) (2-back) tasks and whether such cognitive task performance, compared to a resting state, results in regional deactivation. The location 0-back task, compared to the color 0-back task, activated segregated areas in the frontal, parietal and occipital cortices whereas no differentially activated voxels were obtained when location and color 2-back tasks were directly contrasted. Several midline cortical areas were less active during 0- and 2-back task performance than resting state. The task-induced deactivation increased with task difficulty as demonstrated by larger deactivation during 2-back than 0-back tasks. The results suggest that, in 11-13-year-old children, the visual attentional network is differently recruited by spatial and nonspatial information processing, but the functional organization of cortical activation in WM in this age group is not based on the type of information processed. Furthermore, 11-13-year-old children exhibited a similar pattern of cortical deactivation that has been reported in adults during cognitive task performance compared to a resting state.

Functional magnetic resonance imaging has been widely used to study brain activation induced either by specific sensory stimulation or motor or cognitive task performance. We demonstrate that functional magnetic resonance imaging can provide information of brain regions involved in opioid-induced central nervous system effects. The reproducibility of the responses in the predefined regions of interest was confirmed by repeated boluses of ultra-short acting mu-opioid receptor agonist remifentanil and saline. We report spatially and temporally detailed information after remifentanil administration. Areas rich in mu-opioid receptors showed strong activations, whereas primary somatosensory cortex that has the lowest density of mu-opioid receptors showed negligible activation. The cingulate, orbitofrontal, posterior parietal and insular cortices, and amygdala showed activation, which was temporally closely related to most subjective sensations that were strongest at 80 to 90 s after drug administration. These areas belong to a circuitry that modulates the affective experience of sensory stimuli.

Background: The usefulness of neurofilament light (NfL) as a biomarker for small vessel disease has not been established. We examined the relationship between NfL, neuroimaging changes, and clinical findings in subjects with varying degrees of white matter hyperintensity (WMH). Methods: A subgroup of participants (n = 35) in the Helsinki Small Vessel Disease Study underwent an analysis of NfL in cerebrospinal fluid (CSF) as well as brain magnetic resonance imaging (MRI) and neuropsychological and motor performance assessments. WMH and structural brain volumes were obtained with automatic segmentation. Results: CSF NfL did not correlate significantly with total WMH volume (r = 0.278, p = 0.105). However, strong correlations were observed between CSF NfL and volumes of cerebral grey matter (r = -0.569, p < 0.001), cerebral cortex (r = -0.563, p < 0.001), and hippocampi (r = -0.492, p = 0.003). CSF NfL also correlated with composite measures of global cognition (r = -0.403, p = 0.016), executive functions (r = -0.402, p = 0.017), memory (r = -0.463, p = 0.005), and processing speed (r = -0.386, p = 0.022). Regarding motor performance, CSF NfL was correlated with Timed Up and Go (TUG) test (r = 0.531, p = 0.001), and gait speed (r = -0.450, p = 0.007), but not with single-leg stance. After adjusting for age, associations with volumes in MRI, functional mobility (TUG), and gait speed remained significant, whereas associations with cognitive performance attenuated below the significance level despite medium to large effect sizes. Conclusion: NfL was strongly related to global gray matter and hippocampal atrophy, but not to WMH severity. NfL was also associated with motor performance. Our results suggest that NfL is independently associated with brain atrophy and functional mobility, but is not a reliable marker for cerebral small vessel disease. [ABSTRACT FROM AUTHOR]

Purpose: Severity of white matter lesion (WML) is typically evaluated on magnetic resonance images (MRI), yet the more accessible, faster, and less expensive method is computed tomography (CT). Our objective was to study whether WML can be automatically segmented from CT images using a convolutional neural network (CNN). The second aim was to compare CT segmentation with MRI segmentation. Methods: The brain images from the Helsinki University Hospital clinical image archive were systematically screened to make CT-MRI image pairs. Selection criteria for the study were that both CT and MRI images were acquired within 6 weeks. In total, 147 image pairs were included. We used CNN to segment WML from CT images. Training and testing of CNN for CT was performed using 10-fold cross-validation, and the segmentation results were compared with the corresponding segmentations from MRI. Results: A Pearson correlation of 0.94 was obtained between the automatic WML volumes of MRI and CT segmentations. The average Dice similarity index validating the overlap between CT and FLAIR segmentations was 0.68 for the Fazekas 3 group. Conclusion: CNN-based segmentation of CT images may provide a means to evaluate the severity of WML and establish a link between CT WML patterns and the current standard MRI-based visual rating scale. [ABSTRACT FROM AUTHOR]

Musicians and nonmusicians listened to major, minor, and dissonant musical chords while their BOLD brain responses were registered with functional magnetic resonance imaging. In both groups of listeners, minor and dissonant chords, compared with major chords, elicited enhanced responses in several brain areas, including the amygdala, retrosplenial cortex, brain stem, and cerebellum, during passive listening but not during memorization of the chords. The results indicate that (1) neural processing in emotion-related brain areas is activated even by single chords, (2) emotion processing is enhanced in the absence of cognitive requirements, and (3) musicians and nonmusicians do not differ in their neural responses to single musical chords during passive listening. [ABSTRACT FROM AUTHOR]