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ObjectiveCognitive and motor impairments are the key clinical manifestations of cerebral small vessel disease (SVD), but their interrelations and combined effects on functional outcome have not been elucidated. We investigated the associations between cognitive and motor functions and their interactions and mediating effects on instrumental activities of daily living (IADL) and quality of life in older individuals with various degrees of white matter hyperintensities (WMH).MethodsParticipants of the Helsinki Small Vessel Disease Study (n=152) were assessed according to an extensive clinical, neuropsychological and MRI protocol. Cognitive composite scores for global cognition, processing speed, executive functions and memory were constructed from multiple tests within each domain. Physical examination included measures of gait speed, balance (single-leg-stance) and functional mobility (timed-up-and-go test). IADL was evaluated with a proxy-based Amsterdam IADL questionnaire and quality of life with a self-report EUROHIS-Qol index. Volumes of WMH and gray matter (GM) were obtained with automated segmentation. Sets of linear regression analyses were used to model the associations between motor and cognitive performances, WMH and GM volumes, and IADL and quality of life.ResultsDomain-specific cognitive and motor functions had strong interrelations with each other, and they were significantly associated with IADL, quality of life as well as WMH and GM volumes. A consistent pattern on significant interactions between cognitive and motor functions was found on IADL, but not on quality of life. In particular, low cognitive scores together with decline in the timed-up-and-go test and gait speed were strongly related to impaired IADL. The association of WMH volume with IADL was mediated by global cognition, whereas the association of GM volume with IADL was mediated by global cognition and timed up-and-go performance.ConclusionThe results highlight the complex interplay and synergism between motor and cognitive abilities on functional outcome in SVD. The combined effect of motor and cognitive disturbances on IADL is likely to be greater than the individual effects of each of the two impairments. WMH and brain atrophy contribute to disability through cognitive and motor impairment.

Alpha-pyrrolidinovalerophenone (alpha-PVP) is a designer drug, the mechanism of action of which resembles that of cocaine and amphetamine. New data about the side effects of alpha-PVP are emerging. We present a case report of an acute ischemic stroke following the recreational use of alpha-PVP. The ischemic lesions were located in the middle cerebral artery and deep watershed areas of the left cerebral hemisphere. Occupational therapy and physiotherapy were initiated, and the patient was discharged with only a mild right hemiparesis.

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.

The main objective of this prospective cohort study was to evaluate whether traumatic microbleeds (TMBs) are a significant prognostic factor of return to work (RTW), post-traumatic symptoms, and overall recovery in patients with mild traumatic brain injury (mTBI). One hundred and thirteen patients with mTBI were recruited from the Helsinki University Hospital emergency units. All patients underwent multi-contrast 3T magnetic resonance imaging (MRI) 3-17 days after mTBI. Patients were evaluated in the Traumatic Brain Injury Outpatient Clinic of Helsinki University Hospital 1 month after injury. Post-concussion symptoms were assessed with the Post-Concussion Symptom Questionnaire (RPQ) and overall recovery was assessed with the Glasgow Outcome Scale Extended (GOS-E). Their time to RTW was continuously measured up to 1 year after TBI. Median RTW was 9 days (interquartile range [IQR] 4-30) after mTBI and full RTW rate after 1 year was 98%. Patients with TMBs (n = 22) did not have more post-concussion symptoms (median RPQ 10.0 vs. 7.0, p = 0.217) or worse overall recovery (58% vs. 56% with GOS-E = 8, p = 0.853) than patients without TMBs (n = 91). There was no significant difference in time to RTW (13.5 vs. 7.0 days, p = 0.063). In this study, patients with TMBs did not have delayed RTW or more post-concussion symptoms than other patients with mTBI. TMBs in mTBI do not seem to be a significant prognostic factor of RTW.

Background and purpose: Cerebral small vessel disease is characterized by progressive white matter hyperintensities (WMH) and cognitive decline. However, variability exists in how individuals maintain cognitive capabilities despite significant neuropathology. The relationships between individual cognitive reserve, psychological resilience and cognitive functioning were examined in subjects with varying degrees of WMH. Methods: In the Helsinki Small Vessel Disease Study, 152 subjects (aged 65–75 years) underwent a comprehensive neuropsychological assessment, evaluation of subjective cognitive complaints and brain magnetic resonance imaging with volumetric WMH evaluation. Cognitive reserve was determined by education (years) and the modified Cognitive Reserve Scale (mCRS). Psychological resilience was evaluated with the Resilience Scale 14. Results: The mCRS total score correlated significantly with years of education (r = 0.23, p

Background: We evaluated the prevalence of psychiatric disorders in mild traumatic brain injury (MTBI) patients and investigated psychiatric comorbidity in relation to subjective symptoms and return to work (RTW). Methods: We recruited 103 MTBI patients (mean age 40.8 years, SD 3.1) prospectively from University Hospital. The patients were followed up for one year. The Rivermead Post-Concussion Symptom Questionnaire (RPQ) and Extended Glasgow Outcome Scale (GOSE) were administered one month after MTBI. Three months after MTBI, any psychiatric disorders were assessed using the Structured Clinical Interview for DSM-IV Axis I Disorders. Results: Psychiatric disorders were diagnosed in 26 patients (25.2%). The most common disorders were previous/current depression. At three months, there was no difference between patients with psychiatric disorders versus those without them in RTW (95.7% vs. 87.3%, p = 0.260) or at least in part-time work (100% vs. 94.4%, p = 0.245). In Kaplan–Meier analysis, the median time to RTW was 10 days for both groups. The median RPQ score was 13.0 (Interquartile range (IQR) 6.5–19.0) in patients with a psychiatric disorder compared to 8.5 (IQR 2.3–14.0) in those without one (p = 0.021); respectively, the median GOSE was 7.0 (IQR 7.0–8.0) compared to 8.0 (IQR 7.0–8.0, p = 0.003). Conclusions: Approximately every fourth patient with MTBI had a psychiatric disorder. These patients reported more symptoms, and their functional outcome measured with GOSE at one month after MTBI was worse. However, presence of any psychiatric disorder did not affect RTW. Early contact and adequate follow-up are important when supporting the patient’s return to work.

Highlights • Phase images have artifacts if reconstructed with a vendor’s sum of squares mode. • Quantitative T2* values can be obtained from DICOM data instead of k-space data. • Reconstruction from DICOM data does not reduce white matter/gray matter contrast., Aim and scope A Gradient Echo Plural Contrast Imaging technique (GEPCI) is a post-processing method, which can be used to obtain quantitative T2* values and generate multiple synthetic contrasts from a single acquisition. However, scan duration and image reconstruction from k-space data present challenges in a clinical workflow. This study aimed at optimizing image reconstruction and acquisition duration to facilitate a post-processing method for synthetic image contrast creation in clinical settings. Materials and methods This study consists of tests using the American College of Radiology (ACR) image quality phantom, two healthy volunteers, four mild traumatic brain injury patients and four small vessel disease patients. The measurements were carried out on a 3.0 T scanner with multiple echo times. Reconstruction from k-space data and DICOM data with two different coil-channel combination modes were investigated. Partial Fourier techniques were tested to optimize the scanning time. Conclusions Sum of squares coil-channel combination produced artifacts in phase images, but images created with adaptive combination were artifact-free. The voxel-wise median signed difference of T2* between the vendor’s adaptive channel combination and k-space reconstruction modes was 2.9 ± 0.7 ms for white matter and 4.5 ± 0.6 ms for gray matter. Relative white matter/gray matter contrast of all synthetic images and contrast-to-noise ratio of synthetic T1-weighted images were almost equal between reconstruction modes. Our results indicate that synthetic contrasts can be generated from the vendor’s DICOM data with the adaptive combination mode without affecting the quantitative T2* values or white matter/gray matter contrast.

Supplemental Digital Content is available in the text., 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

Background and purposeCerebral small vessel disease (SVD) is characterized by a wide range of focal and global brain changes. We used automated MRI segmentation to quantify multiple types of SVD brain changes and examined their individual and combined predictive value on cognitive and functional abilities.MethodsMRI scans of 560 subjects of the Leukoaraiosis and Disability Study (LADIS) were analyzed using automated atlas- and convolutional neural network-based segmentation methods yielding volumetric measures of white matter hyperintensities (WMH), lacunes, cortical infarcts, enlarged perivascular spaces 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.ResultsThe strongest predictors of cognitive performance and functional outcome over time were total volumes of WMH, grey matter (GM) and hippocampi (pConclusionsGlobal burden of SVD-related brain changes as quantified by automated image segmentation is a powerful predictor of long-term cognitive decline and functional disability. A combined measure of WMH, lacunar, GM and hippocampal volumes could be used as an imaging identification model of vascular cognitive impairment.

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).

Automated methods for segmentation of ischemic stroke lesions could significantly reduce the workload of radiologists and speed up the beginning of patient treatment. In this paper, we present a method for subacute ischemic stroke lesion segmentation from multispectral magnetic resonance images (MRI). The method involves classification of voxels with a Random Forest algorithm and subsequent classification refinement with contextual clustering. In addition, we utilize the training data to build statistical group-specific templates and use them for calculation of individual voxel-wise differences from the global mean. Our method achieved a Dice coefficient of 0.61 for the leave-one-out cross-validated training data and 0.47 for the testing data of the ISLES challenge 2015.

We studied the effect of use of contextual information on the reproducibility of the results in analysis of fMRI data. We used data from a repeated simple motor fMRI experiment. In the first approach, statistical parametric maps were computed from a spatially unsmoothed data and thresholded using a Bonferroni corrected threshold. In the second approach, the maps were computed from a spatially unsmoothed data but were segmented into nonactive and active regions using a spatial contextual clustering method. In the third approach, the statistical parametric maps were computed from spatially smoothed data and thresholded, using, optionally, a spatial extent threshold. The variation in the classification was largest in the Bonferroni thresholded statistical parametric maps. There were no significant differences in variation between statistical parametric maps generated with all the other methods. In addition to reproducibility, the detection rates of weak simulated activations in the presence of measured scanner and physiological noise were investigated. Contextual clustering method was the most sensitive method, while the least sensitive method was the Bonferroni corrected thresholding. Using simulated data, we demonstrated that the contextual clustering method preserves the shapes of activation regions better than the method using spatial smoothing of the data.

A sudden change in the direction of motion is a particularly salient and relevant feature of visual information. Extensive research has identified cortical areas responsive to visual motion and characterized their sensitivity to different features of motion, such as directional specificity. However, relatively little is known about responses to sudden changes in direction. Electrophysiological data from animals and functional imaging data from humans suggest a number of brain areas responsive to motion, presumably working as a network. Temporal patterns of activity allow the same network to process information in different ways. The present study in humans sought to determine which motion-sensitive areas are involved in processing changes in the direction of motion and to characterize the temporal patterns of processing within this network of brain regions. To accomplish this, we used both magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). The fMRI data were used as supplementary information in the localization of MEG sources. The change in the direction of visual motion was found to activate a number of areas, each displaying a different temporal behavior. The fMRI revealed motion-related activity in areas MT+ (the human homologue of monkey middle temporal area and possibly also other motion sensitive areas next to MT), a region near the posterior end of the superior temporal sulcus (pSTS), V3A, and V1/V2. The MEG data suggested additional frontal sources. An equivalent dipole model for the generators of MEG signals indicated activity in MT+, starting at 130 ms and peaking at 170 ms after the reversal of the direction of motion, and then again at approximately 260 ms. Frontal activity began 0-20 ms later than in MT+, and peaked approximately 180 ms. Both pSTS and FEF+ showed long-duration activity continuing over the latency range of 200-400 ms. MEG responses in the region of V3A and V1/V2 were relatively small, and peaked at longer latencies than the initial peak in MT+. These data revealed characteristic patterns of activity in this cortical network for processing sudden changes in the direction of visual motion.

In this article, we present a novel approach to create single subject fMRI (functional magnetic resonance imaging) activation maps by utilizing the group data of several subjects on the individual level. Classification of a voxel as either activated or non-activated is based on its z-value and classification in the neighborhood. We defined the neighborhood of a voxel to consist of corresponding and neighboring voxels in two subjects in addition to the neighbourhood voxels within the subject itself. Determination of the two neighboring subjects was based on kappa-statistics between single subject activation maps calculated without data from other subjects. This approach was taken using multi-subject contextual clustering. Both fully simulated and real subject null data with simulated activations were used. ROC (receiver operator characteristics) analysis showed increased classification accuracy of activated and non-activated voxels when using the described approach.

To prospectively evaluate magnetoencephalography (MEG) and functional magnetic resonance (MR) imaging, as compared with intraoperative cortical mapping, for identification of the central sulcus.Fifteen patients (six men, nine women; age range, 25-58 years) with a lesion near the primary sensorimotor cortex (13 gliomas, one cavernous hemangioma, and one meningioma) were examined after institutional review board approval and written informed consent from each patient were obtained. At MEG, evoked magnetic fields to median nerve stimulation were recorded; at functional MR imaging, hemodynamic responses to self-paced palmar flexion of the wrist were imaged. General linear model analysis with contextual clustering (P.01) was used to analyze functional MR imaging data, and dipole modeling was used to analyze MEG data. MEG and functional MR localizations were compared with intraoperative cortical mappings. The distance from the area of functional MR imaging activation to the tumor margin was compared between the patients with discordant and those with concordant intraoperative mapping findings by using unpaired t testing.MEG depicted the central sulcus correctly in all 15 patients, as verified at intraoperative mapping. The functional MR imaging localization results agreed with the intraoperative mappings in 11 patients. In all four patients with a false localization, the primary activation was in the postcentral sulcus region, but it did not differ significantly from the primary activation in the patients with correct localization with respect to proximity to the tumor (P = .38). Furthermore, at functional MR imaging, multiple nonprimary areas were activated, with considerable interindividual variation.Although both MEG and functional MR imaging can provide useful information for neurosurgical planning, in the present study, MEG proved to be superior for locating the central sulcus. Activation of multiple nonprimary cerebral areas may confound the interpretation of functional MR imaging results.

Gaussian random field (GRF)-based methods are commonly used for statistical inference and to control the family-wise error rate (FWE) in neuroimaging. They require that the error fields are reasonable lattice approximations to an underlying continuous multivariate Gaussian random field and have differentiable and invertible spatial autocorrelation function. Permutation test estimates the distribution of the test statistic from the data and adjusts automatically for the FWE. Here we present a new analysis procedure, the cluster mass permutation test with contextual enhancement (CMPCE), and compare it to GRF. In CMPCE, the data are first pre-whitened to remove temporal autocorrelations. The FWE rates, the cluster detection probability and delineation accuracy of CMPCE and GRF were compared using measured null data and null data containing simulated activations. We also applied both methods to an fMRI experiment where tactile somatosensory stimulation into the right hand was used. When analyzing the FWE using null data, both CMPCE and GRF gave significantly higher FWEs (CMPCE up to 0.12, GRF up to 0.18) than the nominal significance level 0.05, indicating that the pre-whitening, motion correction or high-pass filtering partially failed. In the simulated activation data, CMPCE gave less falsely classified voxels for the same cluster detection probability level than GRF. The maximal cluster detection probability was on the other hand higher in the GRF-based method. Both methods gave qualitatively similar results in the tactile fMRI data. CMPCE seems to be a promising fMRI analysis method, especially if high delineation accuracy is required.

Ascending sensory impulses play an important role in providing feedback information necessary for the programming of movements. Therefore, it could be expected that sensory input to the cortex is facilitated from a moving limb, as demonstrated in monkeys [1].

The present study evaluated neurophysiologic function of the primary sensorimotor cortex in juvenile neuronal ceroid lipofuscinosis. A 122-channel magnetometer, which allowed studies of the somatosensory system in millimeter and millisecond precision, was used to record somatosensory evoked magnetic fields to median nerve stimulation from 10 patients and their matched control subjects. In both patients and controls, the somatosensory evoked magnetic fields from primary sensorimotor area typically consisted of N20m, P35m, and P60m deflections. In the patients, N20m was significantly delayed, whereas P35m peaked earlier than in the control subjects. The source strengths for N20m and P35m were greater in the patients than in the controls. Both deflections showed a significant positive correlation with the disease duration: the sources were stronger in the older patients than in the younger ones. P60m deflections were normal or reduced in the patients. The results indicated increased thalamocortical excitability in the sensorimotor cortex in juvenile neuronal ceroid lipofuscinosis. (J Child Neurol 1997;12:355-360).

OBJECTIVE: To present applications of magnetoencephalography (MEG) in studies of neurosurgical patients. METHODS: MEG maps magnetic fields generated by electric currents in the brain, and allows the localization of brain areas producing evoked sensory responses and spontaneous electromagnetic activity. The identified sources can be integrated with other imaging modalities, e.g., with magnetic resonance imaging scans of individual patients with brain tumors or intractable epilepsy, or with other types of brain imaging data. RESULTS: MEG measurements using modern whole-scalp instruments assist in tailoring individual therapies for neurosurgical patients by producing maps of functionally irretrievable cortical areas and by identifying cortical sources of interictal and ictal epileptiform activity. The excellent time resolution of MEG enables tracking of complex spaciotemporal source patterns, helping, for example, with the separation of the epileptic pacemaker from propagated activity. The combination of noninvasive mapping of subcortical pathways by magnetic resonance imaging diffusion tensor imaging with MEG source localization will, in the near future, provide even more accurate navigational tools for preoperative planning. Other possible future applications of MEG include the noninvasive estimation of language lateralization and the follow-up of brain plasticity elicited by central or peripheral neural lesions or during the treatment of chronic pain. CONCLUSION: MEG is a mature technique suitable for producing preoperative “road maps” of eloquent cortical areas and for localizing epileptiform activity.

Movement in the visual field is an important part of perceptual information. Previous imaging studies have identified a number of cortical areas sensitive to visual motion [1,2], including what is considered to be the human homologue of area MT/V5 [3,4]. Our study sought to reproduce and expand upon a previous functional magnetic resonance imaging (fMRI) investigation [4] and to examine the timing of activity in the network of regions participating in the processing of visual motion information with magnetoencephalography (MEG).

Somatosensory point localization is a clinical test evaluating spatial accuracy of the somatosensory system. Possible effects of the interstimulus interval (ISI) on point localization threshold have not been previously examined. In the present set of experiments the effect of time delay on somatosensory point localization was studied using ISIs of 1, 3, 5, 7, and 9 s, and applying a newly developed computer-controlled application method of a Semmes-Weinstein monofilament. It was found that the point localization threshold was not significantly affected by the ISI length. However, the response time was shorter and response accuracy better at the shorter (1 and 3 s) than at the longer (5, 7, and 9 s) ISIs, suggesting a change in the mechanism underlying point localization decision criteria in ISIs longer than 3 s.

Presents a contextual clustering procedure for statistical parametric maps (SPM) calculated from time varying three-dimensional images. The algorithm can be used for the detection of neural activations from functional magnetic resonance images (fMRI). An important characteristic of SPM is that the intensity distribution of background (nonactive area) is known whereas the distributions of activation areas are not. The developed contextual clustering algorithm divides an SPM into background and activation areas so that the probability of detecting false activations by chance is controlled, i.e., hypothesis testing is performed. Unlike the much used voxel-by-voxel testing, neighborhood information is utilized, an important difference. This is achieved by using a Markov random field prior and iterated conditional modes (ICM) algorithm. However, unlike in the conventional use of ICM algorithm, the classification is based only on the distribution of background. The results from the authors' simulations and human fMRI experiments using visual stimulation demonstrate that a better sensitivity is achieved with a given specificity in comparison to the voxel-by-voxel thresholding technique. The algorithm is computationally efficient and can be used to detect and delineate objects from a noisy background in other applications.

We report evidence for activation of ipsilateral primary sensorimotor cortex (SMI) after median nerve stimulation recorded with magnetoencephalography (MEG). We measured somatosensory evoked magnetic fields (SEFs) to median nerve stimulation with a 122-channel helmet-shaped magnetometer in 10 healthy subjects. In five, the magnetic field patterns suggested long-latency activation of the ipsilateral SMI. Source locations found by current dipole fitting corresponded to the SMI hand area, as determined by contralateral stimulation. Further evidence for the origin of the ipsilateral responses in SMI was provided by the suppression of these responses during movement of the contralateral fingers. Sensory input to ipsilateral SMI could play a role in sensorimotor integration of bilateral movements.