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Cerebral small vessel disease is the most common cause of vascular cognitive impairment. It typically manifests with lacunar infarcts and ischaemic white matter lesions. However, little is known about how these lesions relate to the cognitive symptoms. Previous studies have found a poor correlation between the burden of ischaemic lesions and cognitive symptoms, thus leaving much of the variance in cognitive performance unexplained. The objective of the current study was to investigate the relationship between the location of subcortical ischaemic lesions and cognitive symptoms in small vessel disease. We applied a voxel-based lesion-symptom mapping approach to data from 215 patients with CADASIL, a genetically defined small vessel disease with mutations in the "NOTCH3" gene. All patients were examined by magnetic resonance imaging and comprehensive neuropsychological testing. Lacunar lesions and white matter lesions were segmented on three-dimensional T[subscript 1] and fluid-attenuated inversion recovery sequences, respectively. One hundred and forty-five subjects had a total of 854 lacunar lesions (range 1-13 per individual). The normalized volume of white matter hyperintensities ranged from 0.0425% to 21.5% of the intracranial cavity. Significant clusters for cognitive performance were detected for both lacunar lesions and white matter hyperintensities. The most prominent results were obtained on a compound score for processing speed, the predominantly affected cognitive domain in this group of patients. Strategic locations included the anterior parts of the thalamus, the genu and anterior limb of the internal capsule, the anterior corona radiata and the genu of the corpus callosum. By combining the lesion-symptom mapping data with information from a probabilistic white matter atlas we found that the majority of the processing speed clusters projected on the anterior thalamic radiation and the forceps minor. In multivariate models that included demographic parameters, brain atrophy and the volume of ischaemic lesions, regional volumes of lacunar lesions and white matter hyperintensities in the anterior thalamic radiation predicted performance in processing speed tasks, whereas there was no independent contribution of the global volume of ischaemic lesions. These observations emphasize the importance of lesion location for both lacunar and ischaemic white matter lesions. Our findings further highlight the anterior thalamic radiation as a major anatomical structure impacting on processing speed. Together these findings provide strong support for a central role of frontal-subcortical circuits in cerebral small vessel disease and vascular cognitive impairment.

Aim: The objective of this study was to investigate which attentional components are of predictive utility in differentiating children with attention-deficit-hyperactivity disorder, combined type (ADHD-C) from their peers without ADHD. Methods: Thirty-four children participated in the study: 17 males with ADHD-C (mean age 10y 4mo, SD 1y 9mo) and 17 comparison children (12 males, 5 females; mean age 10y 8mo, SD 1.7y). Attentional functions were assessed using a computer-administered, child-friendly test series in German (i.e. Testbatterie zur Aufmerksamkeitsprufung fur Kinder; KITAP). The KITAP measures several attentional components, including alertness and executive attention (inhibition, divided attention, flexibility). Results: The variable best able to discriminate between children with and without ADHD-C was found to be response time variability in a go/no go task, followed by, in order, number of errors in a divided attention task and response time variability in an alertness task. However, group discrimination was not facilitated by differences in either response latency or accuracy of response in visuospatial attention and attentional flexibility tasks. Interpretation: The assessment of attentional functions proved to be a powerful instrument for discriminating between children with and without ADHD-C. Notably, the discriminative power of executive attention was found to be task dependent and dependent on processing demands.