Translocator protein (TSPO) genotype does not change cerebrospinal fluid levels of glial activation, axonal and synaptic damage markers in early Alzheimer's disease.

BACKGROUND: PET imaging of the translocator protein (TSPO) is used to assess in vivo brain inflammation. One of the main methodological issues with this method is the allelic dependence of the radiotracer affinity. In Alzheimer's disease (AD), previous studies have shown similar clinical and patho-biological profiles between TSPO genetic subgroups. However, there is no evidence regarding the effect of the TSPO genotype on cerebrospinal-fluid biomarkers of glial activation, and synaptic and axonal damage. METHOD: We performed a trans-sectional study in early AD to compare cerebrospinal-fluid levels of GFAP, YKL-40, sTREM2, IL-6, IL-10, NfL and neurogranin between TSPO genetic subgroups. RESULTS: We recruited 33 patients with early AD including 16 (48%) high affinity binders, 13 (39%) mixed affinity binders, and 4/33 (12%) low affinity binders. No difference was observed in terms of demographics, and cerebrospinal fluid levels of each biomarker for the different subgroups. CONCLUSION: TSPO genotype is not associated with a change in glial activation, synaptic and axonal damage in early AD. Further studies with larger numbers of participants will be needed to confirm that the inclusion of specific TSPO genetic subgroups does not introduce selection bias in studies and trials of AD that combine TSPO imaging with cerebrospinal fluid biomarkers.

Investigating the discrimination ability of 3D convolutional neural networks applied to altered brain MRI parametric maps.

Convolutional neural networks (CNNs) are gradually being recognized in the neuroimaging community as a powerful tool for image analysis. Despite their outstanding performances, some aspects of CNN functioning are still not fully understood by human operators. We postulated that the interpretability of CNNs applied to neuroimaging data could be improved by investigating their behavior when they are fed data with known characteristics. We analyzed the ability of 3D CNNs to discriminate between original and altered whole-brain parametric maps derived from diffusion-weighted magnetic resonance imaging. The alteration consisted in linearly changing the voxel intensity of either one (monoregion) or two (biregion) anatomical regions in each brain volume, but without mimicking any neuropathology. Performing ten-fold cross-validation and using a hold-out set for testing, we assessed the CNNs' discrimination ability according to the intensity of the altered regions, comparing the latter's size and relative position. Monoregion CNNs showed that the larger the modified region, the smaller the intensity increase needed to achieve good performances. Biregion CNNs systematically outperformed monoregion CNNs, but could only detect one of the two target regions when tested on the corresponding monoregion images. Exploiting prior information on training data allowed for a better understanding of CNN behavior, especially when altered regions were combined. This can inform about the complexity of CNN pattern retrieval and elucidate misclassified examples, particularly relevant for pathological data. The proposed analytical approach may serve to gain insights into CNN behavior and guide the design of enhanced detection systems exploiting our prior knowledge.

Robust thalamic nuclei segmentation from T1-weighted MRI using polynomial intensity transformation.

Accurate segmentation of thalamic nuclei, crucial for understanding their role in healthy cognition and in pathologies, is challenging to achieve on standard T1-weighted (T1w) magnetic resonance imaging (MRI) due to poor image contrast. White-matter-nulled (WMn) MRI sequences improve intrathalamic contrast but are not part of clinical protocols or extant databases. In this study, we introduce histogram-based polynomial synthesis (HIPS), a fast preprocessing transform step that synthesizes WMn-like image contrast from standard T1w MRI using a polynomial approximation for intensity transformation. HIPS was incorporated into THalamus Optimized Multi-Atlas Segmentation (THOMAS) pipeline, a method developed and optimized for WMn MRI. HIPS-THOMAS was compared to a convolutional neural network (CNN)-based segmentation method and THOMAS modified for the use of T1w images (T1w-THOMAS). The robustness and accuracy of the three methods were tested across different image contrasts (MPRAGE, SPGR, and MP2RAGE), scanner manufacturers (PHILIPS, GE, and Siemens), and field strengths (3 T and 7 T). HIPS-transformed images improved intra-thalamic contrast and thalamic boundaries, and HIPS-THOMAS yielded significantly higher mean Dice coefficients and reduced volume errors compared to both the CNN method and T1w-THOMAS. Finally, all three methods were compared using the frequently travelling human phantom MRI dataset for inter- and intra-scanner variability, with HIPS displaying the least inter-scanner variability and performing comparably with T1w-THOMAS for intra-scanner variability. In conclusion, our findings highlight the efficacy and robustness of HIPS in enhancing thalamic nuclei segmentation from standard T1w MRI.

MRI hypoperfusion as a determinant of cognitive impairment in adults with Moyamoya angiopathy.

INTRODUCTION: In Moyamoya angiopathy (MMA), mechanisms underlying cognitive impairment remain debated. We aimed to assess the association of cognitive impairment with the degree and the topography of cerebral hypoperfusion in MMA. METHODS: A retrospective analysis of neuropsychological and perfusion MRI data from adults with MMA was performed. Ischemic and haemorrhagic lesion masks were created to account for cerebral lesions in the analysis of cerebral perfusion. Whole brain volume of hypoperfused parenchyma was outlined on perfusion maps using different Tmax thresholds from 4 to 12 s. Regional analysis produced mean Tmax values at different regions of interest. Analyses compared perfusion ratios in patients with and without cognitive impairment, with multivariable logistic regression analysis to identify predictive factors. RESULTS: Cognitive impairment was found in 20/48 (41.7%) patients. Attention/processing speed and memory were equally impaired (24%) followed by executive domain (23%). After adjustment, especially for lesion volume, hypoperfused parenchyma volume outlined by Tmax > 4 s or Tmax > 5 s thresholds was an independent factor of cognitive impairment (OR for Tmax > 4 s = 1.06 [CI 95% 1.008-1.123]) as well as attention/processing speed (OR for Tmax > 4 s = 1.07 [CI 95% 1.003-1.133]) and executive domains (OR for Tmax > 5 s = 1.08 [CI 95% 1.004-1.158]). Regarding cognitive functions, patients with processing speed and flexibility impairment had higher frontal Tmax compared to other ROIs and to patients with normal test scores. DISCUSSION: Cerebral hypoperfusion emerged as an independent factor of cognitive impairment in MMA particularly in attention/processing speed and executive domains, with a strong contribution of frontal areas. CONCLUSION: Considering this association, revascularization surgery could improve cognitive impairment.

Robust thalamic nuclei segmentation from T1-weighted MRI using polynomial intensity transformation.

Accurate segmentation of thalamic nuclei, crucial for understanding their role in healthy cognition and in pathologies, is challenging to achieve on standard T1-weighted (T1w) magnetic resonance imaging (MRI) due to poor image contrast. White-matter-nulled (WMn) MRI sequences improve intrathalamic contrast but are not part of clinical protocols or extant databases. In this study, we introduce histogram-based polynomial synthesis (HIPS), a fast preprocessing transform step that synthesizes WMn-like image contrast from standard T1w MRI using a polynomial approximation for intensity transformation. HIPS was incorporated into THalamus Optimized Multi-Atlas Segmentation (THOMAS) pipeline, a method developed and optimized for WMn MRI. HIPS-THOMAS was compared to a convolutional neural network (CNN)-based segmentation method and THOMAS modified for T1w images (T1w-THOMAS). The robustness and accuracy of the three methods were tested across different image contrasts (MPRAGE, SPGR, and MP2RAGE), scanner manufacturers (PHILIPS, GE, and Siemens), and field strengths (3T and 7T). HIPS-transformed images improved intra-thalamic contrast and thalamic boundaries, and HIPS-THOMAS yielded significantly higher mean Dice coefficients and reduced volume errors compared to both the CNN method and T1w-THOMAS. Finally, all three methods were compared using the frequently travelling human phantom MRI dataset for inter- and intra-scanner variability, with HIPS displaying the least inter-scanner variability and performing comparably with T1w-THOMAS for intra-scanner variability. In conclusion, our findings highlight the efficacy and robustness of HIPS in enhancing thalamic nuclei segmentation from standard T1w MRI.

Neuroimmune activation is associated with neurological outcome in anoxic and traumatic coma.

The pathophysiological underpinnings of critically disrupted brain connectomes resulting in coma are poorly understood. Inflammation is potentially an important but still undervalued factor. Here, we present a first-in-human prospective study using the 18-kDa translocator protein (TSPO) radioligand 18F-DPA714 for PET imaging to allow in vivo neuroimmune activation quantification in patients with coma (n = 17) following either anoxia or traumatic brain injuries in comparison with age- and sex-matched controls. Our findings yielded novel evidence of an early inflammatory component predominantly located within key cortical and subcortical brain structures that are putatively implicated in consciousness emergence and maintenance after severe brain injury (i.e. mesocircuit and frontoparietal networks). We observed that traumatic and anoxic patients with coma have distinct neuroimmune activation profiles, both in terms of intensity and spatial distribution. Finally, we demonstrated that both the total amount and specific distribution of PET-measurable neuroinflammation within the brain mesocircuit were associated with the patient's recovery potential. We suggest that our results can be developed for use both as a new neuroprognostication tool and as a promising biometric to guide future clinical trials targeting glial activity very early after severe brain injury.

Progressive Brain Atrophy in Multiple System Atrophy: A Longitudinal, Multicenter, Magnetic Resonance Imaging Study.

OBJECTIVE: To determine the rates of brain atrophy progression in vivo in patients with multiple system atrophy (MSA). BACKGROUND: Surrogate biomarkers of disease progression are a major unmet need in MSA. Small-scale longitudinal studies in patients with MSA using magnetic resonance imaging (MRI) to assess progression of brain atrophy have produced inconsistent results. In recent years, novel MRI post-processing methods have been developed enabling reliable quantification of brain atrophy in an automated fashion. METHODS: Serial 3D-T1-weighted MRI assessments (baseline and after 1 year of follow-up) of 43 patients with MSA were analyzed and compared to a cohort of early-stage Parkinson's disease (PD) patients and healthy controls (HC). FreeSurfer's longitudinal analysis stream was used to determine the brain atrophy rates in an observer-independent fashion. RESULTS: Mean ages at baseline were 64.4 ± 8.3, 60.0 ± 7.5, and 59.8 ± 9.2 years in MSA, PD patients and HC, respectively. A mean disease duration at baseline of 4.1 ± 2.5 years in MSA patients and 2.3 ± 1.4 years in PD patients was observed. Brain regions chiefly affected by MSA pathology showed progressive atrophy with annual rates of atrophy for the cerebellar cortex, cerebellar white matter, pons, and putamen of -4.24 ± 6.8%, -8.22 ± 8.8%, -4.67 ± 4.9%, and - 4.25 ± 4.9%, respectively. Similar to HC, atrophy rates in PD patients were minimal with values of -0.41% ± 1.8%, -1.47% ± 4.1%, -0.04% ± 1.8%, and -1.54% ± 2.2% for cerebellar cortex, cerebellar white matter, pons, and putamen, respectively. CONCLUSIONS: Patients with MSA show significant brain volume loss over 12 months, and cerebellar, pontine, and putaminal volumes were the most sensitive to change in mid-stage disease. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The long-term impact of irradiation on functional connectivity in brain circuits involved in memory processes after pediatric posterior fossa tumor.

PURPOSE: Memory is one of the main specific cognitive domains impaired with attention and processing speed after a pediatric brain tumor. This work explored the long-term impact of radiotherapy in children with posterior fossa tumor (PFT) on brain connectivity in neural circuits involved in memory using resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: A total of 20 irradiated and 15 non-irradiated PFT survivors, and 21 healthy controls, prospectively included in the IMPALA study (NCT04324450), performed memory tests assessing episodic, procedural, and working memories and were subjected to an rs-fMRI. We manually contoured main structures involved in memory to explore connectivity at rest in a seed-to-voxel analysis. The groups were compared and differences in connectivity were correlated with behavioral scores and irradiation doses. RESULTS: The performance of all mnesic tasks was lower in PFT survivors with a greater alteration in working and episodic memory in irradiated patients. Irradiated survivors had atypical connectivities in all memory circuits compared to controls and in cortico-caudate and cortico-cerebellar circuits compared to non-irradiated survivors. Non-irradiated survivors had only atypical connectivities in the cortico-cerebellar circuits compared to controls. In irradiated survivors, atypical connectivities in cortico-hippocampal circuits were linked with episodic memory scores and dose of irradiation to the left hippocampus and in cortico-striatal circuits with procedural memory scores and dose of irradiation to the striatum. CONCLUSION: The results of this study highlight that irradiation has a long-term impact on brain connectivity in brain circuits involved in memory after pediatric PFT with a specific radiation-dose effect in supratentorial structures.

Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression.

Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this causal depression network (CDN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis Principal Component Analysis (PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CDN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CDN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes (t = -2.35, p = 0.019). This evidence further supports that treatment interventions converge on a CDN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.

Effects of sociodemographic and player characteristics on baseline cognitive performance in 1000 rugby players: A retrospective 8-year follow-up study.

OBJECTIVES: To analyze the effects of sociodemographic and player characteristics on the Sport Concussion Assessment Tool and neuropsychological scores over 8 years in a large sample of rugby players. DESIGN: An 8-year retrospective study of preseason clinical assessments of professional rugby players and players enrolled in training academies at professional clubs. METHODS: The Sport Concussion Assessment Tool-3 or -5, Trail Making Test and Digit Symbol Substitution Test were administered prior to the start of the competition season for each player. Statistical analyses included: (i) descriptive analyses of sociodemographic, player and neuropsychological characteristics; (ii) multivariate models to identify factors influencing cognitive scores at the first visit; and (iii) linear mixed models to assess the evolution of the scores over the years. RESULTS: One thousand players were included (mean age: 22.8, males: 92 %). Twenty-two percent of the athletes reported baseline symptoms. A higher level of education was associated with better cognitive scores at the first visit and over the years. Forwards had poorer processing speed performances compared to backs at the first visit and over repeated assessments. Finally, the number of examinations was associated with improved cognitive scores showing a practice effect on all the neuropsychological tests, except for the Standardized Assessment of Concussion 5th edition. CONCLUSIONS: Results from this retrospective study could help to improve the management of athletes and return-to-play decision-making in collision sports.

Volumetric changes and clinical trajectories in Parkinson's disease: a prospective multicentric study.

BACKGROUND: Longitudinal measures of structural brain changes using MRI in relation to clinical features and progression patterns in PD have been assessed in previous studies, but few were conducted in well-defined and large cohorts, including prospective clinical assessments of both motor and non-motor symptoms. OBJECTIVE: We aimed to identify brain volumetric changes characterizing PD patients, and determine whether regional brain volumetric characteristics at baseline can predict motor, psycho-behavioral and cognitive evolution at one year in a prospective cohort of PD patients. METHODS: In this multicentric 1 year longitudinal study, PD patients and healthy controls from the MPI-R2* cohort were assessed for demographical, clinical and brain volumetric characteristics. Distinct subgroups of PD patients according to motor, cognitive and psycho-behavioral evolution were identified at the end of follow-up. RESULTS: One hundred and fifty PD patients and 73 control subjects were included in our analysis. Over one year, there was no significant difference in volume variations between PD and control subjects, regardless of the brain region considered. However, we observed a reduction in posterior cingulate cortex volume at baseline in PD patients with motor deterioration at one year (p = 0.017). We also observed a bilateral reduction of the volume of the amygdala (p = 0.015 and p = 0.041) and hippocampus (p = 0.015 and p = 0.053) at baseline in patients with psycho-behavioral deterioration, regardless of age, dopaminergic treatment and center. CONCLUSION: Brain volumetric characteristics at baseline may predict clinical trajectories at 1 year in PD as posterior cingulate cortex atrophy was associated with motor decline, while amygdala and hippocampus atrophy were associated with psycho-behavioral decline.

Clinical heterogeneity of neuro-inflammatory PET profiles in early Alzheimer's disease.

The relationship between neuroinflammation and cognition remains uncertain in early Alzheimer's disease (AD). We performed a cross-sectional study to assess how neuroinflammation is related to cognition using TSPO PET imaging and a multi-domain neuropsychological assessment. A standard uptake value ratio (SUVR) analysis was performed to measure [18F]-DPA-714 binding using the cerebellar cortex or the whole brain as a (pseudo)reference region. Among 29 patients with early AD, the pattern of neuroinflammation was heterogeneous and exhibited no correlation with cognition at voxel-wise, regional or whole-brain level. The distribution of the SUVR values was independent of sex, APOE phenotype, early and late onset of symptoms and the presence of cerebral amyloid angiopathy. However, we were able to demonstrate a complex dissociation as some patients with similar PET pattern had opposed neuropsychological profiles while other patients with opposite PET profiles had similar neuropsychological presentation. Further studies are needed to explore how this heterogeneity impacts disease progression.

Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression.

Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this common causal network (CCN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis (Principal Component Analysis, PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CCN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CCN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes. This evidence further supports that treatment interventions converge on a CCN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.

Describing complex disease progression using joint latent class models for multivariate longitudinal markers and clinical endpoints.

Neurodegenerative diseases are characterized by numerous markers of progression and clinical endpoints. For instance, multiple system atrophy (MSA), a rare neurodegenerative synucleinopathy, is characterized by various combinations of progressive autonomic failure and motor dysfunction, and a very poor prognosis. Describing the progression of such complex and multi-dimensional diseases is particularly difficult. One has to simultaneously account for the assessment of multivariate markers over time, the occurrence of clinical endpoints, and a highly suspected heterogeneity between patients. Yet, such description is crucial for understanding the natural history of the disease, staging patients diagnosed with the disease, unravelling subphenotypes, and predicting the prognosis. Through the example of MSA progression, we show how a latent class approach modeling multiple repeated markers and clinical endpoints can help describe complex disease progression and identify subphenotypes for exploring new pathological hypotheses. The proposed joint latent class model includes class-specific multivariate mixed models to handle multivariate repeated biomarkers possibly summarized into latent dimensions and class-and-cause-specific proportional hazard models to handle time-to-event data. Maximum likelihood estimation procedure, validated through simulations is available in the lcmm R package. In the French MSA cohort comprising data of 598 patients during up to 13 years, five subphenotypes of MSA were identified that differ by the sequence and shape of biomarkers degradation, and the associated risk of death. In posterior analyses, the five subphenotypes were used to explore the association between clinical progression and external imaging and fluid biomarkers, while properly accounting for the uncertainty in the subphenotypes membership.

Is pre-radiotherapy metabolic heterogeneity of glioblastoma predictive of progression-free survival?

BACKGROUND AND PURPOSE: All glioblastoma subtypes share the hallmark of aggressive invasion, meaning that it is crucial to identify their different components if we are to ensure effective treatment and improve survival. Proton MR spectroscopic imaging (MRSI) is a noninvasive technique that yields metabolic information and is able to identify pathological tissue with high accuracy. The aim of the present study was to identify clusters of metabolic heterogeneity, using a large MRSI dataset, and determine which of these clusters are predictive of progression-free survival (PFS). MATERIALS AND METHODS: MRSI data of 180 patients acquired in a pre-radiotherapy examination were included in the prospective SPECTRO-GLIO trial. Eight features were extracted for each spectrum: Cho/NAA, NAA/Cr, Cho/Cr, Lac/NAA, and the ratio of each metabolite to the sum of all the metabolites. Clustering of data was performed using a mini-batch k-means algorithm. The Cox model and logrank test were used for PFS analysis. RESULTS: Five clusters were identified as sharing similar metabolic information and being predictive of PFS. Two clusters revealed metabolic abnormalities. PFS was lower when Cluster 2 was the dominant cluster in patients' MRSI data. Among the metabolites, lactate (present in this cluster and in Cluster 5) was the most statistically significant predictor of poor outcome. CONCLUSION: Results showed that pre-radiotherapy MRSI can be used to reveal tumor heterogeneity. Groups of spectra, which have the same metabolic information, reflect the different tissue components representative of tumor burden proliferation and hypoxia. Clusters with metabolic abnormalities and high lactate are predictive of PFS.

Pharmacological Characterization of [18F]-FNM and Evaluation of NMDA Receptors Activation in a Rat Brain Injury Model.

PURPOSE: NMDA receptors (NMDARs) dysfunction plays a central role in the physiopathology of psychiatric and neurodegenerative disorders whose mechanisms are still poorly understood. The development of a PET (positron emission tomography) tracer able to selectively bind to the NMDARs intra-channel PCP site may make it possible to visualize NMDARs in an open and active state. We describe the in vitro pharmacological characterization of [18F]-fluoroethylnormemantine ([18F]-FNM) and evaluate its ability to localize activated NMDA receptors in a rat preclinical model of excitotoxicity. PROCEDURES: The affinity of the non-radioactive analog for the intra-channel PCP site was determined in a radioligand competition assay using [3H]TCP ([3H]N-(1-[thienyl]cyclohexyl)piperidine) on rat brain homogenates. Selectivity was also investigated by the displacement of specific radioligands targeting various cerebral receptors. In vivo brain lesions were performed using stereotaxic quinolinic acid (QA) injections in the left motor area (M1) of seven Sprague Dawley rats. Each rat was imaged with a microPET/CT camera, 40 min after receiving a dose of 30 MBq + / - 20 of [18F]-FNM, 24 and 72 h after injury. Nine non-injured rats were also imaged using the same protocol. RESULTS: FNM displayed IC50 value of 13.0 ± 8.9 µM in rat forebrain homogenates but also showed significant bindings on opioid receptors. In the frontal and left somatosensory areas, [18F]FNM PET detected a mean of 37% and 41% increase in [18F]FNM uptake (p < 0,0001) 24 and 72 h after QA stereotaxic injection, respectively, compared to the control group. CONCLUSIONS: In spite of FNM's poor affinity for NMDAR PCP site, this study supports the ability of this tracer to track massive activation of NMDARs in neurological diseases.

What are the neural correlates of dissociative amnesia? A systematic review of the functional neuroimaging literature.

Aim: Dissociative amnesia is an emblematic psychiatric condition in which patients experience massive memory loss ranging from focal to global amnesia. This condition remains poorly understood and this review aims to investigate the neuroanatomical feature of this disease. Methods: We conducted a systematic review of the scientific literature available on PubMed, up to December 1, 2022, using a combination of keywords referring to dissociative amnesia. We included every scientific report involving patients undergoing a functional imaging procedure. Results: Twenty-two studies met our inclusion criteria (gathering 49 patients). Only one was a controlled study with a large sample. The other 21 were case reports and case series. In resting state, neuroimaging studies mostly showed a hypo-activated right inferolateral prefrontal cortex, associated with limbic hypoactivity and lesser activation of the hippocampal and para-hippocampal structures. The patients also presented abnormal patterns of cerebral activation when performing memory tasks. When testing recognition of memories from the amnestic period, patients showed increased activation across temporal areas (hippocampal and para-hippocampal gyri) and the limbic network. When trying to recollect memories from an amnestic period compared to a non-amnestic period, patients failed to activate these structures efficiently. Most of these patterns tended to return to normal when symptoms resolved. Conclusion: This review identified a paucity of controlled studies in the field of dissociative amnesia neuroimaging, which restricts the extrapolation of results. Patients with dissociative amnesia present a broad prefronto-temporo-limbic network dysfunction. Some of the brain areas implicated in this network might represent potential targets for innovative treatments.

Pseudoprogression in GBM versus true progression in patients with glioblastoma: A multiapproach analysis.

BACKGROUND AND PURPOSE: To investigate the feasibility of using a multiapproach analysis combining clinical data, diffusion- and perfusion-weighted imaging, and 3D magnetic resonance spectroscopic imaging to distinguish true tumor progression (TP) from pseudoprogression (PSP) in patients with glioblastoma. MATERIALS AND METHODS: Progression was suspected within 6 months of radiotherapy in 46 of the 180 patients included in the Phase-III SpectroGlio trial (NCT01507506). Choline/creatine (Cho/Cr), choline/N-acetyl aspartate (Cho/NAA) and lactate/N-acetyl aspartate (Lac/NAA) ratios were extracted. Apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps were calculated. ADC, relative CBV values and tumor volume (TV) were collected at relapse. Differences between TP and PSP were evaluated using Mann-Whitney tests, and p values were adjusted with Bonferroni correction. RESULTS: Patients with suspected progression underwent a new MRI scan 1 month after the first one. Of these, 28 were classified as PSP, and 18 as TP. After a median follow-up of 41 months, median overall survival was higher in PSP than in TP (25.2 vs 20.3 months; p = 0.0092). Lac/NAA and Cho/Cr ratios were higher in TP than in PSP (1.2 vs 0.5; p = 0.006; and 3 vs 2.2; p = 0.021). After multivariate regression analysis, TV was the most significant predictor of TP vs PSP, and the only one retained in the model (p = 0.028). CONCLUSION: Three spectroscopic ratios could be used to differentiate PSP from TP. TV at relapse was the most predictive factor in the multivariate analysis, and overall survival was higher in PSP than in TP.