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The purpose of this study was to establish a non-invasive clinical PET/MR protocol using [18F]-labeled deoxyglucose (FDG) that provides physicians with regional metabolic rate of glucose (MRGlc) values and to clarify the contribution of absolute quantification to clinical management of patients with non-lesional extratemporal lobe epilepsy (ETLE). The study included a group of 15 patients with non-lesional ETLE who underwent a dynamic FDG PET study using a fully-integrated PET/MRI system (Siemens Biograph). FDG tracer uptake images were converted to MRGlc (μmol/100 g/min) maps using an image derived input function that was extracted based on the combined analysis of PET and MRI data. In addition, the same protocol was applied to a group of healthy controls, yielding a normative database. Abnormality maps for ETLE patients were created with respect to the normative database, defining significant hypo- or hyper-metabolic regions that exceeded ±2 SD of normal regional mean MRGlc values. Abnormality maps derived from MRGlc images of ETLE patients contributed to the localization of hypo-metabolic areas against visual readings in 53% and increased the confidence in the original clinical readings in 33% of all cases. Moreover, quantification allowed identification of hyper-metabolic areas that are associated with frequently spiking cortex, rarely acknowledged in clinical readings. Overall, besides providing some confirmatory information to visual readings, quantitative PET imaging demonstrated only a moderate impact on clinical management of patients with complex pathology that leads to epileptic seizures, failing to provide new decisive information that would have changed classification of patients from being rejected to being considered for surgical intervention.

Time series analysis with delay differential equations (DDEs) reveals nonlinear properties of the underlying dynamical system and can serve as a non-linear time-domain classification tool. Here global DDE models were used to analyze short segments of simulated time series from a known dynamical system, the Rössler system, in high noise regimes. In a companion paper, we apply the DDE model developed here to classify short segments of encephalographic (EEG) data recorded from patients with Parkinson's disease and healthy subjects. Nine simulated subjects in each of two distinct classes were generated by varying the bifurcation parameter b and keeping the other two parameters (a and c) of the Rössler system fixed. All choices of b were in the chaotic parameter range. We diluted the simulated data using white noise ranging from 10dB to -30dB signal-to-noise ratios (SNR). Structure selection was supervised by selecting the number of terms, delays, and order of nonlinearity of the model DDE model that best linearly separated the two classes of data. The distances d from the linear dividing hyperplane was then used to assess the classification performance by computing the area A' under the ROC curve. The selected model was tested on untrained data using repeated random sub-sampling validation. DDEs were able to accurately distinguish the two dynamical conditions, and moreover, to quantify the changes in the dynamics. There was a significant correlation between the dynamical bifurcation parameter b of the simulated data and the classification parameter d from our analysis. This correlation still held for new simulated subjects with new dynamical parameters selected from each of the two dynamical regimes. Furthermore, the correlation was robust to added noise, being significant even when the noise was greater than the signal. We conclude that DDE models may be used as a generalizable and reliable classification tool for even small segments of noisy data.

Traumatic brain injury (TBI) is a multidimensional and highly complex disease commonly resulting in widespread injury to axons, due to rapid inertial acceleration/deceleration forces transmitted to the brain during impact. Axonal injury leads to brain network dysfunction, significantly contributing to cognitive and functional impairments frequently observed in TBI survivors. Diffuse axonal injury (DAI) is a clinical entity suggested by impaired level of consciousness and coma on clinical examination and characterized by widespread injury to the hemispheric white matter tracts, the corpus callosum and the brain stem. The clinical course of DAI is commonly unpredictable and it remains a challenging entity with limited therapeutic options, to date. Although axonal integrity may be disrupted at impact, the majority of axonal pathology evolves over time, resulting from delayed activation of complex intracellular biochemical cascades. Activation of these secondary biochemical pathways may lead to axonal transection, named secondary axotomy, and be responsible for the clinical decline of DAI patients. Advances in the neurocritical care of TBI patients have been achieved by refinements in multimodality monitoring for prevention and early detection of secondary injury factors, which can be applied also to DAI. There is an emerging role for biomarkers in blood, cerebrospinal fluid, and interstitial fluid using microdialysis in the evaluation of axonal injury in TBI. These biomarker studies have assessed various axonal and neuroglial markers as well as inflammatory mediators, such as cytokines and chemokines. Moreover, modern neuroimaging can detect subtle or overt DAI/white matter changes in diffuse TBI patients across all injury severities using magnetic resonance spectroscopy, diffusion tensor imaging, and positron emission tomography. Importantly, serial neuroimaging studies provide evidence for evolving axonal injury. Since axonal injury may be a key risk factor for neurodegeneration and dementias at long-term following TBI, the secondary injury processes may require prolonged monitoring. The aim of the present review is to summarize the clinical short- and long-term monitoring possibilities of axonal injury in TBI. Increased knowledge of the underlying pathophysiology achieved by advanced clinical monitoring raises hope for the development of novel treatment strategies for axonal injury in TBI.

In recent years there has been a significant increase in the use of electroencephalography (EEG) and magnetoencephalography (MEG) to investigate changes in oscillatory brain activity associated with tinnitus with many conflicting results. Current view of the underlying mechanism of tinnitus is that it results from changes in brain activity in various structures of the brain as a consequence of sensory deprivation. This in turn gives rise to increased spontaneous activity and/or synchrony in the auditory centres but also involves modulation from non-auditory processes from structures of the limbic and paralimbic system. Some of the neural changes associated with tinnitus may be assessed non-invasively in humans with MEG and EEG (M/EEG) in ways which are superior to animal studies and other non-invasive imaging techniques. However, both MEG and EEG have their limitations and research results can be misinterpreted in the absence of sufficient understanding of these limitations. In this article, I intend to provide a brief review of these techniques, describe what the recorded signals reflect in terms of the underlying neural activity, and their strengths and limitations. I also discuss some pertinent methodological issues involved in tinnitus related studies and concludes with suggestions to minimise possible discrepancies between results. The overall message is that while MEG and EEG are extremely useful techniques, the interpretation of results from tinnitus studies requires much caution given the individual variability in oscillatory activity and the limits of these techniques.

Psychotic symptoms frequently occur in Parkinson's disease (PD), but their pathophysiology is poorly understood. According to the National Institute of Health RDoc programme, the pathophysiological basis of neuropsychiatric symptoms may be better understood in terms of dysfunction of underlying domains of neurocognition in a trans-diagnostic fashion. Abnormal cortico-striatal reward processing has been proposed as a key domain contributing to the pathogenesis of psychotic symptoms in schizophrenia. This theory has received empirical support in the study of schizophrenia spectrum disorders and preclinical models of psychosis, but has not been tested in the psychosis associated with PD. We, therefore, investigated brain responses associated with reward expectation and prediction error signaling during reinforcement learning in PD-associated psychosis. An instrumental learning task with monetary gains and losses was conducted during an fMRI study in PD patients with (n = 12), or without (n = 17), a history of psychotic symptoms, along with a sample of healthy controls (n = 24). We conducted region of interest analyses in the ventral striatum (VS), ventromedial prefrontal and posterior cingulate cortices, and whole-brain analyses. There was reduced activation in PD patients with a history of psychosis, compared to those without, in the posterior cingulate cortex and the VS during reward anticipation (p < 0.05 small volume corrected). The results suggest that cortical and striatal abnormalities in reward processing, a putative pathophysiological mechanism of psychosis in schizophrenia, may also contribute to the pathogenesis of psychotic symptoms in PD. The finding of posterior cingulate dysfunction is in keeping with prior results highlighting cortical dysfunction in the pathogenesis of PD psychosis.

Background and Objectives: Persistent post-concussive symptoms (PCS) consist of neurologic and psychological complaints persisting after a mild traumatic brain injury (mTBI). It affects up to 50% of mTBI patients, may cause long-term disability, and reduce patients' quality of life. The aim of this review was to examine the possible use of different neuroimaging modalities in PCS. Methods: Articles from Pubmed database were screened to extract studies that investigated the relationship between any neuroimaging features and symptoms of PCS. Descriptive statistics were applied to report the results. Results: A total of 80 out of 939 papers were included in the final review. Ten examined conventional MRI (30% positive finding), 24 examined diffusion weighted imaging (54.17% positive finding), 23 examined functional MRI (82.61% positive finding), nine examined electro(magneto)encephalography (77.78% positive finding), and 14 examined other techniques (71% positive finding). Conclusion: MRI was the most widely used technique, while functional techniques seem to be the most sensitive tools to evaluate PCS. The common functional patterns associated with symptoms of PCS were a decreased anti-correlation between the default mode network and the task positive network and reduced brain activity in specific areas (most often in the prefrontal cortex). Significance: Our findings highlight the importance to use functional approaches which demonstrated a functional alteration in brain connectivity and activity in most studies assessing PCS.

Objective: 1) To determine the brain connectivity pattern associated with clinical rigidity scores in Parkinson's disease (PD) and 2) to determine the relation between clinically-assessed rigidity and quantitative metrics of motor performance.Background: Rigidity, the resistance to passive movement, is exacerbated in PD by asking the subject to move the contralateral limb, implying that rigidity involves a distributed brain network. Rigidity mainly affects subjects when they attempt to move; yet the relation between clinical rigidity scores and quantitative aspects of motor performance are unknown.Methods: Ten clinically diagnosed PD patients (off medication) and ten controls were recruited to perform an fMRI squeeze-bulb tracking task that included both visually guided and internally guided features. The direct functional connectivity between anatomically defined regions of interest was assessed with Dynamic Bayesian Networks (DBNs). Tracking performance was assessed by fitting Linear Dynamical System (LDS) models to the motor performance, and was compared to the clinical rigidity scores. A cross-validated Least Absolute Shrinkage and Selection Operator (LASSO) regression method was used to determine the brain connectivity network that best predicted clinical rigidity scores.Results: The damping ratio of the LDS models significantly correlated with clinical rigidity scores (p < 10-4). An fMRI connectivity network in subcortical and primary and premotor cortical regions accurately predicted clinical rigidity scores (p < 10-5). Conclusions: A widely distributed cortical/subcortical network is associated with rigidity observed in PD patients, which reinforces the importance of altered functional connectivity in the pathophysiology of PD. PD subjects with higher rigidity scores tend to have less overshoot in their tracking performance, and damping ratio may represent a robust, quantitative marker of the motoric effects of increasing rigidity.

Objective: To evaluate local and distant blood oxygen level dependent (BOLD) signal changes related to interictal epileptiform discharges (IED) in drug-resistant temporal lobe epilepsy (TLE).Methods: Thirty-three TLE patients undergoing EEG–functional Magnetic Resonance Imaging (fMRI) as part of the presurgical workup were consecutively enrolled. First, a single-subject spike-related analysis was performed: (a) to verify the BOLD concordance with the presumed Epileptogenic Zone (EZ); and (b) to investigate the Intrinsic Connectivity Networks (ICN) involvement. Then, a group analysis was performed to search for common BOLD changes in TLE.Results: Interictal epileptiform discharges were recorded in 25 patients and in 19 (58%), a BOLD response was obtained at the single-subject level. In 42% of the cases, BOLD changes were observed in the temporal lobe, although only one patient had a pure concordant finding, with a single fMRI cluster overlapping (and limited to) the EZ identified by anatomo-electro-clinical correlations. In the remaining 58% of the cases, BOLD responses were localized outside the temporal lobe and the presumed EZ. In every patient, with a spike-related fMRI map, at least one ICN appeared to be involved. Four main ICNs were preferentially involved, namely, motor, visual, auditory/motor speech, and the default mode network. At the single-subject level, EEG–fMRI proved to have high specificity (above 65%) in detecting engagement of an ICN and the corresponding ictal/postictal symptom, and good positive predictive value (above 67%) in all networks except the visual one. Finally, in the group analysis of BOLD changes related to IED revealed common activations at the right precentral gyrus, supplementary motor area, and middle cingulate gyrus.Significance: Interictal temporal spikes affect several distant extra-temporal areas, and specifically the motor/premotor cortex. EEG–fMRI in patients with TLE eligible for surgery is recommended not for strictly localizing purposes rather it might be useful to investigate ICNs alterations at the single-subject level.

Neuroimaging in myotonic dystrophies provided a major contribution to the insight into brain involvement which is highly prevalent in these multisystemic disorders. Particular in Myotonic Dystrophy Type 1, conventional MRI first revealed hyperintense white matter lesions, predominantly localized in the anterior temporal lobe. Brain atrophy and ventricle enlargement were additional early findings already described almost 30 years ago. Since then, more advanced and sophisticated imaging methods have been applied in Myotonic Dystrophy Types 1 and 2. Involvement of actually normal appearing white matter and widespread cortical affection in PET studies were key results toward the recognition of diffuse and not only focally localized brain pathology in vivo. Later, structural abnormalities of both, gray and white matter, have been found in both forms of the disorder, albeit more prominent in myotonic dystrophy type 1. In Type 1, a consistent widespread cortical and subcortical involvement of gray and white matter affecting all lobes, brainstem and cerebellum was observed. Spectroscopy studies gave additional evidence of neuronal and glial damage in both types. Central questions regarding the origin and spatiotemporal evolution of the CNS involvement and its relevance for clinical symptoms had already been raised 30 years ago, however are still not answered. Results of correlation analyses between neuroimaging and clinical parameters are diverse and with few exceptions not well reproducible across studies. It may be related to the fact that most of the reported studies included only small numbers of subjects, sometimes even not separating Myotonic Dystrophy Type 1 from Type 2. But this heterogeneity may also support the current point of view that the clinical impairments are not simply linked to specific and regionally circumscribed structural or functional brain alterations. It seems more convincing that disturbed networks build the functional and structural substrate of clinical symptoms in these disorders as it is proposed in other neuropsychiatric diseases. Consecutively, structural and functional network analyses may provide additional information regarding the link between brain pathology and clinical symptoms. Up to now, only cross-sectional neuroimaging studies have been published. To analyze the temporal evolution of brain affection, longitudinal studies are urgently needed, and systematic natural history data would be useful to identify potential biomarkers for therapeutic studies.