Clinical variables contributing to the identification of biologically defined subgroups within cognitively unimpaired and mild cognitive impairment individuals.

BACKGROUND: A lack of consensus exists in linking demographic, behavioral, and cognitive characteristics to biological stages of dementia, defined by the ATN (amyloid, tau, neurodegeneration) classification incorporating amyloid, tau, and neuronal injury biomarkers. METHODS: Using a random forest classifier we investigated whether 27 demographic, behavioral, and cognitive characteristics allowed distinction between ATN-defined groups with the same cognitive profile. This was done separately for three cognitively unimpaired (CU) (112 A-T-N-; 46 A+T+N+/-; 65 A-T+/-N+/-) and three mild cognitive impairment (MCI) (128 A-T-N-; 223 A+T+N+/-; 94 A-T+/-N+/-) subgroups. RESULTS: Classification-balanced accuracy reached 39% for the CU and 52% for the MCI subgroups. Logical Delayed Recall (explaining 16% of the variance), followed by the Alzheimer's Disease Assessment Scale 13 (14%) and Everyday Cognition Informant (10%), were the most relevant characteristics for classification of the MCI subgroups. Race and ethnicity, marital status, and Everyday Cognition Patient were not relevant (0%). CONCLUSIONS: The demographic, behavioral, and cognitive measures used in our model were not informative in differentiating ATN-defined CU profiles. Measures of delayed memory, general cognition, and activities of daily living were the most informative in differentiating ATN-defined MCI profiles; however, these measures alone were not sufficient to reach high classification performance.

Instrumented Gait Classification Using Meaningful Features in Patients with Impaired Coordination.

Early onset ataxia (EOA) and developmental coordination disorder (DCD) both affect cerebellar functioning in children, making the clinical distinction challenging. We here aim to derive meaningful features from quantitative SARA-gait data (i.e., the gait test of the scale for the assessment and rating of ataxia (SARA)) to classify EOA and DCD patients and typically developing (CTRL) children with better explainability than previous classification approaches. We collected data from 18 EOA, 14 DCD and 29 CTRL children, while executing both SARA gait tests. Inertial measurement units were used to acquire movement data, and a gait model was employed to derive meaningful features. We used a random forest classifier on 36 extracted features, leave-one-out-cross-validation and a synthetic oversampling technique to distinguish between the three groups. Classification accuracy, probabilities of classification and feature relevance were obtained. The mean classification accuracy was 62.9% for EOA, 85.5% for DCD and 94.5% for CTRL participants. Overall, the random forest algorithm correctly classified 82.0% of the participants, which was slightly better than clinical assessment (73.0%). The classification resulted in a mean precision of 0.78, mean recall of 0.70 and mean F1 score of 0.74. The most relevant features were related to the range of the hip flexion-extension angle for gait, and to movement variability for tandem gait. Our results suggest that classification, employing features representing different aspects of movement during gait and tandem gait, may provide an insightful tool for the differential diagnoses of EOA, DCD and typically developing children.

2D Gait Skeleton Data Normalization for Quantitative Assessment of Movement Disorders from Freehand Single Camera Video Recordings.

Overlapping phenotypic features between Early Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) can complicate the clinical distinction of these disorders. Clinical rating scales are a common way to quantify movement disorders but in children these scales also rely on the observer's assessment and interpretation. Despite the introduction of inertial measurement units for objective and more precise evaluation, special hardware is still required, restricting their widespread application. Gait video recordings of movement disorder patients are frequently captured in routine clinical settings, but there is presently no suitable quantitative analysis method for these recordings. Owing to advancements in computer vision technology, deep learning pose estimation techniques may soon be ready for convenient and low-cost clinical usage. This study presents a framework based on 2D video recording in the coronal plane and pose estimation for the quantitative assessment of gait in movement disorders. To allow the calculation of distance-based features, seven different methods to normalize 2D skeleton keypoint data derived from pose estimation using deep neural networks applied to freehand video recording of gait were evaluated. In our experiments, 15 children (five EOA, five DCD and five healthy controls) were asked to walk naturally while being videotaped by a single camera in 1280 × 720 resolution at 25 frames per second. The high likelihood of the prediction of keypoint locations (mean = 0.889, standard deviation = 0.02) demonstrates the potential for distance-based features derived from routine video recordings to assist in the clinical evaluation of movement in EOA and DCD. By comparison of mean absolute angle error and mean variance of distance, the normalization methods using the Euclidean (2D) distance of left shoulder and right hip, or the average distance from left shoulder to right hip and from right shoulder to left hip were found to better perform for deriving distance-based features and further quantitative assessment of movement disorders.

Quantification of Movement in Stroke Patients under Free Living Conditions Using Wearable Sensors: A Systematic Review.

Stroke is a main cause of long-term disability worldwide, placing a large burden on individuals and health care systems. Wearable technology can potentially objectively assess and monitor patients outside clinical environments, enabling a more detailed evaluation of their impairment and allowing individualization of rehabilitation therapies. The aim of this review is to provide an overview of setups used in literature to measure movement of stroke patients under free living conditions using wearable sensors, and to evaluate the relation between such sensor-based outcomes and the level of functioning as assessed by existing clinical evaluation methods. After a systematic search we included 32 articles, totaling 1076 stroke patients from acute to chronic phases and 236 healthy controls. We summarized the results by type and location of sensors, and by sensor-based outcome measures and their relation with existing clinical evaluation tools. We conclude that sensor-based measures of movement provide additional information in relation to clinical evaluation tools assessing motor functioning and both are needed to gain better insight in patient behavior and recovery. However, there is a strong need for standardization and consensus, regarding clinical assessments, but also regarding the use of specific algorithms and metrics for unsupervised measurements during daily life.

Intermuscular coherence analysis in older adults reveals that gait-related arm swing drives lower limb muscles via subcortical and cortical pathways.

KEY POINTS: Gait-related arm swing in humans supports efficient lower limb muscle activation, indicating a neural coupling between the upper and lower limbs during gait. Intermuscular coherence analyses of gait-related electromyography from upper and lower limbs in 20 healthy participants identified significant coherence in alpha and beta/gamma bands indicating that upper and lower limbs share common subcortical and cortical drivers that coordinate the rhythmic four-limb gait pattern. Additional directed connectivity analyses revealed that upper limb muscles drive and shape lower limb muscle activity during gait via subcortical and cortical pathways and to a lesser extent vice versa. The results provide a neural underpinning that arm swing may serve as an effective rehabilitation therapy concerning impaired gait in neurological diseases. ABSTRACT: Human gait benefits from arm swing, as it enhances efficient lower limb muscle activation in healthy participants as well as patients suffering from neurological impairment. The underlying neuronal mechanisms of such coupling between upper and lower limbs remain poorly understood. The aim of the present study was to examine this coupling by intermuscular coherence analysis during gait. Additionally, directed connectivity analysis of this coupling enabled assessment of whether gait-related arm swing indeed drives lower limb muscles. To that end, electromyography recordings were obtained from four lower limb muscles and two upper limb muscles bilaterally, during gait, of 20 healthy participants (mean (SD) age 67 (6.8) years). Intermuscular coherence analysis revealed functional coupling between upper and lower limb muscles in the alpha and beta/gamma band during muscle specific periods of the gait cycle. These effects in the alpha and beta/gamma bands indicate involvement of subcortical and cortical sources, respectively, that commonly drive the rhythmic four-limb gait pattern in an efficiently coordinated fashion. Directed connectivity analysis revealed that upper limb muscles drive and shape lower limb muscle activity during gait via subcortical and cortical pathways and to a lesser extent vice versa. This indicates that gait-related arm swing reflects the recruitment of neuronal support for optimizing the cyclic movement pattern of the lower limbs. These findings thus provide a neural underpinning for arm swing to potentially serve as an effective rehabilitation therapy concerning impaired gait in neurological diseases.

Rhythmic neural activity is comodulated with short-term gait modifications during first-time use of a dummy prosthesis: a pilot study.

BACKGROUND: After transfemoral amputation, many hours of practice are needed to re-learn walking with a prosthesis. The long adaptation process that consolidates a novel gait pattern seems to depend on cerebellar function for reinforcement of specific gait modifications, but the precise, step-by-step gait modifications (e.g., foot placement) most likely rely on top-down commands from the brainstem and cerebral cortex. The aim of this study was to identify, in able-bodied individuals, the specific modulations of cortical rhythms that accompany short-term gait modifications during first-time use of a dummy prosthesis. METHODS: Fourteen naïve participants walked on a treadmill without (one block, 4 min) and with a dummy prosthesis (three blocks, 3 × 4 min), while ground reaction forces and 32-channel EEG were recorded. Gait cycle duration, stance phase duration, step width, maximal ground reaction force and, ground reaction force trace over time were measured to identify gait modifications. Independent component analysis of EEG data isolated brain-related activity from distinct anatomical sources. The source-level data were segmented into gait cycles and analyzed in the time-frequency domain to reveal relative enhancement or suppression of intrinsic cortical oscillations. Differences between walking conditions were evaluated with one-way ANOVA and post-hoc testing (α = 0.05). RESULTS: Immediate modifications occurred in the gait parameters when participants were introduced to the dummy prosthesis. Except for gait cycle duration, these modifications remained throughout the duration of the experimental session. Power modulations of the theta, mu, beta, and gamma rhythms, of sources presumably from the fronto-central and the parietal cortices, were found across the experimental session. Significant power modulations of the theta, beta, and gamma rhythms within the gait cycle were predominately found around the heel strike of both feet and the swing phase of the right (prosthetic) leg. CONCLUSIONS: The modulations of cortical activity could be related to whole-body coordination, including the swing phase and placing of the prosthesis, and the bodyweight transfer between legs and arms. Reduced power modulation of the gamma rhythm within the experimental session may indicate initial motor memories being formed. Better understanding of the sensorimotor processes behind gait modifications may inform the development of neurofeedback strategies to assist gait rehabilitation.

Functional Neural Correlates of Anosognosia in Mild Cognitive Impairment and Alzheimer's Disease: a Systematic Review.

Functional neuroimaging techniques (i.e. single photon emission computed tomography, positron emission tomography, and functional magnetic resonance imaging) have been used to assess the neural correlates of anosognosia in mild cognitive impairment (MCI) and Alzheimer's disease (AD). A systematic review of this literature was performed, following the Preferred Reporting Items for Systematic Reviews and Meta Analyses statement, on PubMed, EMBASE, and PsycINFO databases. Twenty-five articles met all inclusion criteria. Specifically, four brain connectivity and 21 brain perfusion, metabolism, and activation articles. Anosognosia is associated in MCI with frontal lobe and cortical midline regional dysfunction (reduced perfusion and activation), and with reduced parietotemporal metabolism. Reduced within and between network connectivity is observed in the default mode network regions of AD patients with anosognosia compared to AD patients without anosognosia and controls. During initial stages of cognitive decline in anosognosia, reduced indirect neural activity (i.e. perfusion, metabolism, and activation) is associated with the cortical midline regions, followed by the parietotemporal structures in later stages and culminating in frontotemporal dysfunction. Although the current evidence suggests differences in activation between AD or MCI patients with anosognosia and healthy controls, more evidence is needed exploring the differences between MCI and AD patients with and without anosognosia using resting state and task related paradigms.

N1 lateralization and dyslexia: An event-related potential study in children with a familial risk of dyslexia.

The rapid automatic specialized processing of printed words is signalled by the left-lateralization of the N1 component in the visual event-related potential (ERP). In the present study, we have investigated whether differences in N1 lateralization can be observed between Dutch children with and without (a familial risk of) dyslexia around the age of 12 years using a linguistic judgement task. Forty-five participants were included in the ERP analysis, 18 in the low familial risk group without dyslexia, 15 in the high familial risk group without dyslexia, and 12 in the high familial risk group with dyslexia. The results showed that although the N1 peaked slightly earlier in the left hemisphere, the N1 amplitude was right-lateralized in all groups. Moreover, there were no group differences in N1 amplitude or latency, and there was no relationship between reading (related) test scores and N1 characteristics. The results of the present study and our previous findings in adults suggest that print-tuning lateralization is a process that is still developing in adolescence. Because other studies did find N1 lateralization in younger readers with a print versus nonprint contrast, the current results seem to indicate that differences in N1 lateralization also depend on the experimental paradigm.

Somatosensory electrical stimulation improves skill acquisition, consolidation, and transfer by increasing sensorimotor activity and connectivity.

The interaction between the somatosensory and motor systems is important for normal human motor function and learning. Enhancing somatosensory input using somatosensory electrical stimulation (SES) can increase motor performance, but the neuronal mechanisms underlying these effects are largely unknown. With EEG, we examined whether skill acquisition, consolidation, and interlimb transfer after SES was related to increased activity in sensorimotor regions, as assessed by the N30 somatosensory evoked potential or rather increased connectivity between these regions, as assessed by the phase slope index (PSI). Right- and left-hand motor performance and EEG measures were taken before, immediately after, and 24 h ( day 2) after either SES ( n = 12; 5 men) or Control ( n = 12; 5 men). The results showed skill acquisition and consolidation in the stimulated right hand immediately after SES (6%) and on day 2 (9%) and interlimb transfer to the nonstimulated left hand on day 2 relative to Control (8%, all P < 0.05). Increases in N30 amplitudes correlated with skill acquisition while PSI from electrodes that represent the posterior parietal and primary somatosensory cortex to the electrode representing the primary motor cortex correlated with skill consolidation. In contrast, interlimb transfer did not correlate with the EEG-derived neurophysiological estimates obtained in the present study, which may indicate the involvement of subcortical structures in interlimb transfer after SES. In conclusion, weak peripheral somatosensory inputs in the form of SES improve skill acquisition, consolidation, and interlimb transfer that coincide with different cortical adaptations, including enhanced N30 amplitudes and PSI. NEW & NOTEWORTHY The relationship between adaptations in synaptic plasticity and motor learning following somatosensory electrical stimulation (SES) is incompletely understood. Here, we used for the first time a multifactorial approach that examined skill acquisition, consolidation, and interlimb transfer following 20 min of SES. In addition, we quantified sensorimotor integration and the magnitude and direction of connectivity with EEG. Following artificial electrical stimulation, increases in sensorimotor integration and connectivity were found to correlate with skill acquisition and consolidation, respectively.

Visualisation of the 'Optimal Cerebral Perfusion' Landscape in Severe Traumatic Brain Injury Patients.

OBJECTIVE: An 'optimal' cerebral perfusion pressure (CPPopt) can be defined as the point on the CPP scale corresponding to the greatest autoregulatory capacity. This can be established by examining the pressure reactivity index PRx-CPP relationship, which is approximately U-shaped but suffers from noise and missing data. In this paper, we present a method for plotting the whole PRx-CPP relationship curve against time in the form of a colour-coded map depicting the 'landscape' of that relationship extending back for several hours and to display this robustly at the bedside.This is a short version of a full paper recently published in Critical Care Medicine (2016) containing some new insights and details of a novel bedside implementation based on a presentation during Intracranial Pressure 2016 Symposium in Boston. METHODS: Recordings from routine monitoring of traumatic brain injury patients were processed using ICM+. Time-averaged means for arterial blood pressure, intracranial pressure, cerebral perfusion pressure (CPP) and pressure reactivity index (PRx) were calculated and stored with time resolution of 1 min. ICM+ functions have been extended to include not just an algorithm of automatic calculation of CPPopt but also the 'CPPopt landscape' chart. RESULTS: Examining the 'CPPopt landscape' allows the clinician to differentiate periods where the autoregulatory range is narrow and needs to be targeted from periods when the patient is generally haemodynamically stable, allowing for more relaxed CPP management. This information would not have been conveyed using the original visualisation approaches. CONCLUSIONS: We describe here a natural extension to the concept of autoregulatory assessment, providing the retrospective 'landscape' of the PRx-CPP relationship extending over the past several hours. We have incorporated such visualisation techniques online in ICM+. The proposed visualisation may facilitate clinical evaluation and use of autoregulation-guided therapy.

Enhanced Visualization of Optimal Cerebral Perfusion Pressure Over Time to Support Clinical Decision Making.

OBJECTIVE: Cerebrovascular reactivity can provide a continuously updated individualized target for management of cerebral perfusion pressure, termed optimal cerebral perfusion pressure. The objective of this project was to find a way of improving the optimal cerebral perfusion pressure methodology by introducing a new visualization method. DATA SOURCES: Four severe traumatic brain injury patients with intracranial pressure monitoring. DATA EXTRACTION: Data were collected and pre-processed using ICM+ software. DATA SYNTHESIS: Sequential optimal cerebral perfusion pressure curves were used to create a color-coded maps of autoregulation - cerebral perfusion pressure relationship evolution over time. CONCLUSIONS: The visualization method addresses some of the main drawbacks of the original methodology and might bring the potential for its clinical application closer.

Validation of "laboratory-supported" criteria for functional (psychogenic) tremor.

BACKGROUND: In a small group of patients, we have previously shown that a combination of electrophysiological tests was able to distinguish functional (psychogenic) tremor and organic tremor with excellent sensitivity and specificity. OBJECTIVES: This study aims to validate an electrophysiological test battery as a tool to diagnose patients with functional tremor with a "laboratory-supported" level of certainty. METHODS: For this prospective data collection study, we recruited 38 new patients with functional tremor (mean age 37.9 ± 24.5 years; mean disease duration 5.9 ± 9.0 years) and 73 new patients with organic tremor (mean age 55.4 ± 25.4 years; mean disease duration 15.8 ± 17.7 years). Tremor was recorded at rest, posture (with and without loading), action, while performing tapping tasks (1, 3, and 5 Hz), and while performing ballistic movements with the less-affected hand. Electrophysiological tests were performed by raters blinded to the clinical diagnosis. We calculated a sum score for all performed tests (maximum of 10 points) and used a previously suggested cut-off score of 3 points for a diagnosis of laboratory-supported functional tremor. RESULTS: We demonstrated good interrater reliability and test-retest reliability. Patients with functional tremor had a higher average score on the test battery when compared with patients with organic tremor (3.6 ± 1.4 points vs 1.0 ± 0.8 points; P < .001), and the predefined cut-off score for laboratory-supported functional tremor yielded a test sensitivity of 89.5% and a specificity of 95.9%. CONCLUSION: We now propose this test battery as the basis of laboratory-supported criteria for the diagnosis of functional tremor, and we encourage its use in clinical and research practice.

Motor network disruption in essential tremor: a functional and effective connectivity study.

Although involvement of the cerebello-thalamo-cortical network has often been suggested in essential tremor, the source of oscillatory activity remains largely unknown. To elucidate mechanisms of tremor generation, it is of crucial importance to study the dynamics within the cerebello-thalamo-cortical network. Using a combination of electromyography and functional magnetic resonance imaging, it is possible to record the peripheral manifestation of tremor simultaneously with brain activity related to tremor generation. Our first aim was to study the intrinsic activity of regions within the cerebello-thalamo-cortical network using dynamic causal modelling to estimate effective connectivity driven by the concurrently recorded tremor signal. Our second aim was to objectify how the functional integrity of the cerebello-thalamo-cortical network is affected in essential tremor. We investigated the functional connectivity between cerebellar and cortical motor regions showing activations during a motor task. Twenty-two essential tremor patients and 22 healthy controls were analysed. For the effective connectivity analysis, a network of tremor-signal related regions was constructed, consisting of the left primary motor cortex, premotor cortex, supplementary motor area, left thalamus, and right cerebellar motor regions lobule V and lobule VIII. A measure of variation in tremor severity over time, derived from the electromyogram, was included as modulatory input on intrinsic connections and on the extrinsic cerebello-thalamic connections, giving a total of 128 models. Bayesian model selection and random effects Bayesian model averaging were used. Separate seed-based functional connectivity analyses for the left primary motor cortex, left supplementary motor area and right cerebellar lobules IV, V, VI and VIII were performed. We report two novel findings that support an important role for the cerebellar system in the pathophysiology of essential tremor. First, in the effective connectivity analysis, tremor variation during the motor task has an excitatory effect on both the extrinsic connection from cerebellar lobule V to the thalamus, and the intrinsic activity of cerebellar lobule V and thalamus. Second, the functional integrity of the motor network is affected in essential tremor, with a decrease in functional connectivity between cortical and cerebellar motor regions. This decrease in functional connectivity, related to the motor task, correlates with an increase in clinical tremor severity. Interestingly, increased functional connectivity between right cerebellar lobules I-IV and the left thalamus correlates with an increase in clinical tremor severity. In conclusion, our findings suggest that cerebello-dentato-thalamic activity and cerebello-cortical connectivity is disturbed in essential tremor, supporting previous evidence of functional cerebellar changes in essential tremor.

A Brain-Wide Study of Age-Related Changes in Functional Connectivity.

Aging affects functional connectivity between brain areas, however, a complete picture of how aging affects integration of information within and between functional networks is missing. We used complex network measures, derived from a brain-wide graph, to provide a comprehensive overview of age-related changes in functional connectivity. Functional connectivity in young and older participants was assessed during resting-state fMRI. The results show that aging has a large impact, not only on connectivity within functional networks but also on connectivity between the different functional networks in the brain. Brain networks in the elderly showed decreased modularity (less distinct functional networks) and decreased local efficiency. Connectivity decreased with age within networks supporting higher level cognitive functions, that is, within the default mode, cingulo-opercular and fronto-parietal control networks. Conversely, no changes in connectivity within the somatomotor and visual networks, networks implicated in primary information processing, were observed. Connectivity between these networks even increased with age. A brain-wide analysis approach of functional connectivity in the aging brain thus seems fundamental in understanding how age affects integration of information.

Print-Tuning Lateralization and Handedness: an Event-Related Potential Study in Dyslexic Higher Education Students.

Despite their ample reading experience, higher education students with dyslexia still show deficits in reading and reading-related skills. Lateralized print tuning, the early sensitivity to print of the left parietal cortex signalled by the N1 event-related potential (ERP) component, differs between beginning dyslexic readers and controls. For adults, the findings are mixed. The present study aims to investigate whether print tuning, as indexed by the N1 component, differs between 24 students with dyslexia and 15 non-dyslexic controls. Because handedness has been linked to lateralization, first, a separate analysis was conducted including only right-handed participants (n = 12 in both groups), like in most previous studies. ERPs were measured during a judgement task, requiring visual, phonological, or semantic judgments. In both groups, the N1 was earlier and stronger in the left than in the right hemisphere. However, when only strongly right-handed participants were evaluated, the N1 was less left-lateralized for participants with dyslexia as compared with controls. Participants with dyslexia had longer reaction times during the ERP experiment and performed worse on many reading (-related) tasks. These findings suggest that abnormal print tuning can still be found among higher education students with dyslexia and that handedness should be regarded in the study of print tuning.

cTBS delivered to the left somatosensory cortex changes its functional connectivity during rest.

The primary somatosensory cortex (SI) plays a critical role in somatosensation as well as in action performance and social cognition. Although the SI has been a major target of experimental and clinical research using non-invasive transcranial magnetic stimulation (TMS), to date information on the effect of TMS over the SI on its resting-state functional connectivity is very scant. Here, we explored whether continuous theta burst stimulation (cTBS), a repetitive TMS protocol, administered over the SI can change the functional connectivity of the brain at rest, as measured using resting-state functional magnetic resonance imaging (rs-fMRI). In a randomized order on two different days we administered active TMS or sham TMS over the left SI. TMS was delivered off-line before scanning by means of cTBS. The target area was selected previously and individually for each subject as the part of the SI activated both when the participant executes and observes actions. Three analytical approaches, both theory driven (partial correlations and seed based whole brain regression) and more data driven (Independent Component Analysis), indicated a reduction in functional connectivity between the stimulated part of the SI and several brain regions functionally associated with the SI including the dorsal premotor cortex, the cerebellum, basal ganglia, and anterior cingulate cortex. These findings highlight the impact of cTBS delivered over the SI on its functional connectivity at rest. Our data may have implications for experimental and therapeutic applications of cTBS over the SI.

Brain mechanisms underlying the effects of aging on different aspects of selective attention.

The ability to suppress irrelevant information declines with age, while the ability to enhance relevant information remains largely intact. We examined mechanisms behind this dissociation in an fMRI study, using a selective attention task in which relevant and irrelevant information appeared simultaneously. Slowing of response times due to distraction by irrelevant targets was larger in older than younger participants. Increased distraction was related to larger increases in activity and connectivity in areas of the dorsal attention network, indicating a more pronounced (re-)orientation of attention. The decreases in accuracy in target compared to nontarget trials were smaller in older compared to younger participants. In older adults we found increased recruitment of areas in the fronto-parietal control network (FPCN) during target detection. Moreover, older adults showed increased connectivity between the FPCN, supporting cognitive control, and somatomotor areas implicated in response selection and execution. This connectivity increase was related to improved target detection, suggesting that older adults engage additional cognitive control, which might enable the observed intact performance in detecting and responding to target stimuli.

Reduced specificity of functional connectivity in the aging brain during task performance.

The importance of studying connectivity in the aging brain is increasingly recognized. Recent studies have shown that connectivity within the default mode network is reduced with age and have demonstrated a clear relation of these changes with cognitive functioning. However, research on age-related changes in other functional networks is sparse and mainly focused on prespecified functional networks. Using functional magnetic resonance imaging, we investigated age-related changes in functional connectivity during a visual oddball task in a range of functional networks. It was found that compared with young participants, elderly showed a decrease in connectivity between areas belonging to the same functional network. This was found in the default mode network and the somatomotor network. Moreover, in all identified networks, elderly showed increased connectivity between areas within these networks and areas belonging to different functional networks. Decreased connectivity within functional networks was related to poorer cognitive functioning in elderly. The results were interpreted as a decrease in the specificity of functional networks in older participants.

Flexible connectivity in the aging brain revealed by task modulations.

Recent studies have shown that aging has a large impact on connectivity within and between functional networks. An open question is whether elderly still have the flexibility to adapt functional network connectivity (FNC) to the demands of the task at hand. To study this, we collected fMRI data in younger and older participants during resting state, a selective attention (SA) task and an n-back working memory task with varying levels of difficulty. Spatial independent component (IC) analysis was used to identify functional networks over all participants and all conditions. Dual regression was used to obtain participant and task specific time-courses per IC. Subsequently, functional connectivity was computed between all ICs in each of the tasks. Based on these functional connectivity matrices, a scaled version of the eigenvector centrality (SEC) was used to measure the total influence of each IC in the complete graph of ICs. The results demonstrated that elderly remain able to adapt FNC to task demands. However, there was an age-related shift in the impetus for FNC change. Older participants showed the maximal change in SEC patterns between resting state and the SA task. Young participants, showed the largest shift in SEC patterns between the less demanding SA task and the more demanding 2-back task. Our results suggest that increased FNC changes from resting state to low demanding tasks in elderly reflect recruitment of additional resources, compared with young adults. The lack of change between the low and high demanding tasks suggests that elderly reach a resource ceiling.

Haemosync: A synchronisation algorithm for multimodal haemodynamic signals.

BACKGROUND: Synchronous acquisition of haemodynamic signals is crucial for their multimodal analysis, such as dynamic cerebral autoregulation (DCA) analysis of arterial blood pressure (ABP) and transcranial Doppler (TCD)-derived cerebral blood velocity (CBv). Several technical problems can, however, lead to (varying) time-shifts between the different signals. These can be difficult to recognise and can strongly influence the multimodal analysis results. METHODS: We have developed a multistep, cross-correlation-based time-shift detection and synchronisation algorithm for multimodal pulsatile haemodynamic signals. We have developed the algorithm using ABP and CBv measurements from a dataset that contained combinations of several time-shifts. We validated the algorithm on an external dataset with time-shifts. We additionally quantitatively validated the algorithm's performance on a dataset with artificially added time-shifts, consisting of sample clock differences ranging from -0.2 to 0.2 s/min and sudden time-shifts between -4 and 4 s. The influence of superimposed noise and variation in waveform morphology on the time-shift estimation was quantified, and their influence on DCA-indices was determined. RESULTS: The instantaneous median absolute error (MedAE) between the artificially added time-shifts and the estimated time-shifts was 12 ms (median, IQR 12-12, range 11-14 ms) for drifts between -0.1 and 0.1 s/min and sudden time-shifts between -4 and 4 s. For drifts above 0.1 s/min, MedAE was higher (median 753, IQR 19 - 766, range 13 - 772 ms). When a certainty threshold was included (peak cross-correlation > 0.9), MedAE for all drifts-shift combinations decreased to 12 ms, with smaller variability (IQR 12 - 13, range 8 - 22 ms, p < 0.001). The time-shift estimation is robust to noise, as the MedAE was similar for superimposed white noise with variance equal to the signal variance. After time-shift correction, DCA-indices were similar to the original, non-time-shifted signals. Phase shift differed by 0.17° (median, IQR 0.13-0.2°, range 0.0038-1.1°) and 0.54° (median, IQR 0.23-1.7°, range 0.0088-5.6°) for the very low frequency and low frequency ranges, respectively. DISCUSSION: This algorithm allows visually interpretable detection and accurate correction of time-shifts between pulsatile haemodynamic signals (ABP and CBv).