Controlling target brain regions by optimal selection of input nodes.

The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inappropriate modeling of brain dynamics, and an overly ambitious focus on whole-brain activity control. In this work, we leverage recent progress in linear modeling of brain dynamics (effective connectivity) and we exploit the concept of target controllability to focus on the control of a single region or a small subnetwork of nodes. We discuss when control may be possible with a reasonably low energy cost and few stimulation loci, and give general predictions on where to stimulate depending on the subset of regions one wishes to control. Importantly, using the robustly asymmetric effective connectome instead of the symmetric structural connectome (as in previous research), we highlight the fundamentally different roles in- and out-hubs have in the control problem, and the relevance of inhibitory connections. The large degree of inter-individual variation in the effective connectome implies that the control problem is best formulated at the individual level, but we discuss to what extent group results may still prove useful.

Increased Levels of the Parkinson's Disease-Associated Gene ITPKB Correlate with Higher Expression Levels of α-Synuclein, Independent of Mutation Status.

Autosomal dominant mutations in the gene encoding α-synuclein (SNCA) were the first to be linked with hereditary Parkinson's disease (PD). Duplication and triplication of SNCA has been observed in PD patients, together with mutations at the N-terminal of the protein, among which A30P and A53T influence the formation of fibrils. By overexpressing human α-synuclein in the neuronal system of Drosophila, we functionally validated the ability of IP3K2, an ortholog of the GWAS identified risk gene, Inositol-trisphosphate 3-kinase B (ITPKB), to modulate α-synuclein toxicity in vivo. ITPKB mRNA and protein levels were also increased in SK-N-SH cells overexpressing wild-type α-synuclein, A53T or A30P mutants. Kinase overexpression was detected in the cytoplasmatic and in the nuclear compartments in all α-synuclein cell types. By quantifying mRNAs in the cortex of PD patients, we observed higher levels of ITPKB mRNA when SNCA was expressed more (p < 0.05), compared to controls. A positive correlation was also observed between SNCA and ITPKB expression in the cortex of patients, which was not seen in the controls. We replicated this observation in a public dataset. Our data, generated in SK-N-SH cells and in cortex from PD patients, show that the expression of α-synuclein and ITPKB is correlated in pathological situations.

Variability of regional glucose metabolism and the topology of functional networks in the human brain.

The brain consumes the most energy per relative mass amongst the organs in the human body. Theoretical and empirical studies have shown that behavioral processes are relatively inexpensive metabolically, and that most energy goes to maintaining the status quo, i.e., the balance of cell membranes' resting potentials and subthreshold spontaneous activity. Spontaneous activity fluctuates across brain regions in a correlated fashion that defines multi-scale hierarchical networks called resting-state networks (RSNs). Different regions of the brain display different metabolic consumption, but the relationship between regional brain metabolism and RSNs is still under investigation. Here, we examine the variability of glucose metabolism across brain regions, measured with the relative standard uptake value (SUVR) using 18F-FDG PET, and the topology of RSNs, measured through graph analysis applied to fMRI resting-state functional connectivity (FC). We found a moderate linear relationship between the strength (STR) of pairwise regional FC and metabolism. Moreover, the linear correlation between SUVR and STR grew stronger as we considered more connected regions (hubs). Regions connecting different RSNs, or connector hubs, showed higher SUVR than regions connecting nodes within the same RSN, or provincial hubs. Our results show that functional connections as probed by fMRI are related to glucose metabolism, especially in a system of provincial and connector hubs.

Unveiling whole-brain dynamics in normal aging through Hidden Markov Models.

During normal aging, the brain undergoes structural and functional changes. Many studies applied static functional connectivity (FC) analysis on resting state functional magnetic resonance imaging (rs-fMRI) data showing a link between aging and the increase of between-networks connectivity. However, it has been demonstrated that FC is not static but varies over time. By employing the dynamic data-driven approach of Hidden Markov Models, this study aims to investigate how aging is related to specific characteristics of dynamic brain states. Rs-fMRI data of 88 subjects, equally distributed in young and old were analyzed. The best model resulted to be with six states, which we characterized not only in terms of FC and mean BOLD activation, but also uncertainty of the estimates. We found two states were mostly occupied by young subjects, whereas three other states by old subjects. A graph-based analysis revealed a decrease in strength with the increase of age, and an overall more integrated topology of states occupied by old subjects. Indeed, while young subjects tend to cycle in a loop of states characterized by a high segregation of the networks, old subjects' loops feature high integration, with a crucial intermediary role played by the dorsal attention network. These results suggest that the employed mathematical approach captures the complex and rich brain's dynamics underpinning the aging process.

Brain PET and functional MRI: why simultaneously using hybrid PET/MR systems?

INTRODUCTION: In the last 20 years growing attention has been devoted to multimodal imaging. The recent literature is rich of clinical and research studies that have been performed using different imaging modalities on both separate and integrated positron emission tomography (PET) and magnetic resonance (MR) scanners. However, today, hybrid PET/MR systems measure signals related to brain structure, metabolism, neurochemistry, perfusion, and neuronal activity simultaneously, i.e. in the same physiological conditions. A frequently raised question at meeting and symposia is: "Do we really need a hybrid PET/MR system? Are there any advantages over acquiring sequential and separate PET and MR scans?" The present paper is an attempt to answer these questions specifically in relation to PET combined with functional magnetic resonance imaging (fMRI) and arterial spin labeling. EVIDENCE ACQUISITION: We searched (last update: June 2017) the databases PubMed, PMC, Google Scholar and Medline. We also included additional studies if they were cited in the selected articles. No language restriction was applied to the search, but the reviewed articles were all in English. Among all the retrieved articles, we selected only those performed using a hybrid PET/MR system. We found a total of 17 papers that were selected and discussed in three main groups according to the main radiopharmaceutical used: 18F-fluorodeoxyglucose (18F-FDG) (N.=8), 15O-water (15O-H2O) (N.=3) and neuroreceptors (N.=6). EVIDENCE SYNTHESIS: Concerning studies using 18F-FDG, simultaneous PET/fMRI revealed that global aspects of functional organization (e.g. graph properties of functional connections) are partially associated with energy consumption. There are remarkable spatial and functional similarities across modalities, but also discrepant findings. More work is needed on this point. There are only a handful of papers comparing blood flow measurements with PET 15O-H2O and MR arterial spin label (ASL) measures, and they show significant regional CBF differences between these two modalities. However, at least in one study the correlation at the level of gray, white matter, and whole brain is rather good (r=0.94, 0.8, 0.81 respectively). Finally, receptor studies show that simultaneous PET/fMRI could be a useful tool to characterize functional connectivity along with dynamic neuroreceptor adaptation in several physiological (e.g. working memory) or pathological (e.g. pain) conditions, with or without drug administrations. CONCLUSIONS: The simultaneous acquisition of PET (using a number of radiotracers) and functional MRI (using a number of sequences) offers exciting opportunities that we are just beginning to explore. The results thus far are promising in the evaluation of cerebral metabolism/flow, neuroreceptor adaptation, and network's energetic demand.

The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity?

Brain glucose metabolism, which can be investigated at the macroscale level with [18F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain's spontaneous activity can describe local metabolism is unknown. Here, using two multimodal datasets of healthy participants, we built a multivariable multilevel model of functional-metabolic associations, assessing multiple functional features, describing the 1) rs-fMRI signal, 2) hemodynamic response, 3) static and 4) time-varying functional connectivity, as predictors of the human brain's metabolic architecture. The full model was trained on one dataset and tested on the other to assess its reproducibility. We found that functional-metabolic spatial coupling is nonlinear and heterogeneous across the brain, and that local measures of rs-fMRI activity and synchrony are more tightly coupled to local metabolism. In the testing dataset, the degree of functional-metabolic spatial coupling was also related to peripheral metabolism. Overall, although a significant proportion of regional metabolic variability can be described by measures of spontaneous activity, additional efforts are needed to explain the remaining variance in the brain's 'dark energy'.

The dynamic functional connectivity fingerprint of high-grade gliomas.

Resting state fMRI has been used in many studies to investigate the impact of brain tumours on functional connectivity (FC). However, these studies have so far assumed that FC is stationary, disregarding the fact that the brain fluctuates over dynamic states. Here we utilised resting state fMRI data from 33 patients with high-grade gliomas and 33 healthy controls to examine the dynamic interplay between resting-state networks and to gain insights into the impact of brain tumours on functional dynamics. By employing Hidden Markov Models, we demonstrated that functional dynamics persist even in the presence of a high-grade glioma, and that patients exhibited a global decrease of connections strength, as well as of network segregation. Furthermore, through a multivariate analysis, we demonstrated that patients' cognitive scores are highly predictive of pathological dynamics, thus supporting our hypothesis that functional dynamics could serve as valuable biomarkers for better understanding the traits of high-grade gliomas.

Case report: Multiple disconnection patterns revealed by a multi-modal analysis explained behavior after a focal frontal damage.

Introduction: There is overwhelming evidence that focal lesions cause structural, metabolic, functional, and electrical disconnection of regions directly and indirectly connected with the site of injury. Unfortunately, methods to study disconnection (positron emission tomography, structural and functional magnetic resonance imaging, electroencephalography) have been applied primarily in isolation without capturing their interaction. Moreover, multi-modal imaging studies applied to focal lesions are rare. Case report: We analyzed with a multi-modal approach the case of a patient presenting with borderline cognitive deficits across multiple domains and recurrent delirium. A post-surgical focal frontal lesion was evident based on the brain anatomical MRI. However, we were able to acquire also simultaneous MRI (structural and functional) and [18F]FDG using a hybrid PET/MRI scan along with EEG recordings. Despite the focality of the primary anatomical lesion, structural disconnection in the white matter bundles extended far beyond the lesion and showed a topographical match with the cortical glucose hypometabolism seen both locally and remotely, in posterior cortices. Similarly, a right frontal delta activity near/at the region of structural damage was associated with alterations of distant occipital alpha power. Moreover, functional MRI revealed even more widespread local and distant synchronization, involving also regions not affected by the structural/metabolic/electrical impairment. Conclusion: Overall, this exemplary multi-modal case study illustrates how a focal brain lesion causes a multiplicity of disconnection and functional impairments that extend beyond the borders of the anatomical irrecoverable damage. These effects were relevant to explain patient's behavior and may be potential targets of neuro-modulation strategies.

A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling.

Metabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [18F]FDG signals, i.e., within-individual MC (wi-MC), as for resting-state fMRI functional connectivity (FC). The validity and interpretability of both approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel wi-MC methodology; 2) compare ai-MC maps from standardized uptake value ratio (SUVR) vs. [18F]FDG kinetic parameters fully describing the tracer behavior (i.e., Ki, K1, k3); 3) assess MC interpretability in comparison to structural connectivity and FC. We developed a new approach based on Euclidean distance to calculate wi-MC from PET time-activity curves. The across-individual correlation of SUVR, Ki, K1, k3 produced different networks depending on the chosen [18F]FDG parameter (k3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47-0.63) than for ai-MC (0.24-0.39). Our analyses demonstrate that calculating individual-level MC from dynamic PET is feasible and yields interpretable matrices that bear similarity to fMRI FC measures.

Trans-synaptic degeneration in the optic pathway: Exploring the role of lateral geniculate nucleus in early stages of relapsing-remitting multiple sclerosis.

BACKGROUND: Optic pathway is considered an ideal model to study the interaction between inflammation and neurodegeneration in multiple sclerosis (MS). METHODS: Optical Coherence Tomography (OCT) and 3.0 T magnetic resonance imaging (MRI) were acquired in 92 relapsing remitting (RR) MS at clinical onset. Peripapillary RNFL (pRNFL) and macular layers were measured. White matter (WM) and gray matter (GM) lesion volumes (LV), lateral geniculate nucleus (LGN) volume, optic radiations (OR) WM LV, thickness of pericalcarine cortex were evaluated. OCT and MRI control groups (healthy controls [HC]-OCT and HC-MRI) were included. RESULTS: A significant thinning of temporal pRNFL and papillo-macular bundle (PMB) was observed (p<0.001) in 16 (17%) patients presented with monocular optic neuritis (MSON+), compared to 76 MSON- and 30 HC (-15 µm). In MSON-, PMB was reduced (-3 µm) compared to HC OCT (p<0.05). INL total volume was increased both in MSON+ (p<0.001) and MSON- (p = 0.033). Inner retinal layers volumes (macular RNFL, GCL and IPL) were significantly decreased in MSON+ compared to HC (p<0.001) and MSON- (p<0.001). Reduced GCL volume in the parafoveal ring was observed in MSON- compared to HCOCT (p < 0.05). LGN volume was significantly reduced only in MSON+ patients compared to HC-MRI (p<0.001) and MSON- (p<0.007). GCL, IPL and GCIP volumes associated with ipsilateral LGN volume in MSON+ and MSON-. Finally, LGN volume associated with visual cortex thickness with no significant difference between MSON+ and MSON-. CONCLUSIONS: Anterograde trans-synaptic degeneration is early detectable in RRMS presenting with optic neuritis but does not involve LGN.

Quantification of normal-appearing white matter damage in early relapse-onset multiple sclerosis through neurite orientation dispersion and density imaging.

Background Neurodegeneration is a major contributor of neurological disability in multiple sclerosis (MS). The possibility to fully characterize normal appearing white matter (NAWM) damage could provide the missing information needed to clarify the mechanisms beyond disability accumulation. Objective In the present study we aimed to characterize the presence and extent of NAWM damage and its correlation with clinical disability. Methods We applied Diffusion Tensor Imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI) in a cohort of 27 early relapse-onset MS patients (disease duration < 5 years) compared to a population of 26 age- and sex-matched healthy controls (HCs). All patients underwent a neurological examination, including the Expanded Disability Status Scale (EDSS). Results MS patients showed lower fractional anisotropy (FA) and higher mean diffusivity (MD) values in the main WM bundles, such as the corticospinal tract, corpus callosum, superior and middle cerebellar peduncles, posterior thalamic radiation (which includes optic radiation), cingulum and external capsule. All brain areas with reduced FA/increased MD also displayed a reduction in neurite density index (NDI). However, comparing individual voxels of the WM skeleton between MS and HCs, a higher number of NDI significant voxels was disclosed compared to FA/MD (56.4% vs 11.2%/41.2%). No significant correlations were observed between DTI/NODDI metrics and EDSS. Conclusions Our findings suggest that NDI may allow for a better characterization and understanding of the microstructural changes in the NAWM since the early relapsing-remitting MS phases. Future longitudinal studies including a larger cohort of patients with different clinical phenotypes may clarify the association between NODDI metrics and disability progression.

Image-derived Input Function in brain [18F]FDG PET data: which alternatives to the carotid siphons?

Quantification of brain [18F] fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) data requires an input function. A noninvasive alternative to gold-standard arterial sampling is the image-derived input function (IDIF), typically extracted from the internal carotid arteries (ICAs), which are however difficult to segment and subjected to spillover effects. In this work, we evaluated the feasibility of extracting the IDIF from two different vascular sites, i.e., 1) common carotids (CCA) and 2) superior sagittal sinus (SSS), other than 3) ICA in a large group of glioma patients undergoing a dynamic [18F]FDG PET acquisition on a hybrid PET/MR scanner. Comparisons are drawn between the different IDIFs in terms of peak amplitude and shape, as well as between the estimates of fractional uptake rate (Kr) obtained from the different extraction sites in terms of a) grey/white matter average absolute values, b) ratio of grey-to-white matter, and c) spatial patterns for the hemisphere contralateral to the lesion. Clinical Relevance - This work points towards new feasible IDIF extraction sites (CCA in particular) which could allow for fully noninvasive absolute PET quantification in clinical populations.

Modeling venous plasma samples in [18F] FDG PET studies: a nonlinear mixed-effects approach.

The gold-standard approach to quantifying dynamic PET images relies on using invasive measures of the arterial plasma tracer concentration. An attractive alternative is to employ an image-derived input function (IDIF), corrected for spillover effects and rescaled with venous plasma samples. However, venous samples are not always available for every participant. In this work, we used the nonlinear mixed-effects modeling approach to develop a model which infers venous tracer kinetics by using venous samples obtained from a population of healthy individuals and integrating subject-specific covariates. Population parameters (fixed effects), their between-subject variability (random effects), and the effects of covariates were estimated. The selected model will allow to reliably infer venous tracer kinetics in subjects with missing measurements. Clinical relevance - The derived model will be relevant for fully noninvasive dynamic FDG PET quantification using image-derived input functions in both healthy and patient populations when hemodynamics is not impaired.

Widespread cortical functional disconnection in gliomas: an individual network mapping approach.

Assessment of impaired/preserved cortical regions in brain tumours is typically performed via intraoperative direct brain stimulation of eloquent areas or task-based functional MRI. One main limitation is that they overlook distal brain regions or networks that could be functionally impaired by the tumour. This study aims (i) to investigate the impact of brain tumours on the cortical synchronization of brain networks measured with resting-state functional magnetic resonance imaging (resting-state networks) both near the lesion and remotely and (ii) to test whether potential changes in resting-state networks correlate with cognitive status. The sample included 24 glioma patients (mean age: 58.1 ± 16.4 years) with different pathological staging. We developed a new method for single subject localization of resting-state networks abnormalities. First, we derived the spatial pattern of the main resting-state networks by means of the group-guided independent component analysis. This was informed by a high-resolution resting-state networks template derived from an independent sample of healthy controls. Second, we developed a spatial similarity index to measure differences in network topography and strength between healthy controls and individual brain tumour patients. Next, we investigated the spatial relationship between altered networks and tumour location. Finally, multivariate analyses related cognitive scores across multiple cognitive domains (attention, language, memory, decision making) with patterns of multi-network abnormality. We found that brain gliomas cause broad alterations of resting-state networks topography that occurred mainly in structurally normal regions outside the tumour and oedema region. Cortical regions near the tumour often showed normal synchronization. Finally, multi-network abnormalities predicted attention deficits. Overall, we present a novel method for the functional localization of resting-state networks abnormalities in individual glioma patients. These abnormalities partially explain cognitive disabilities and shall be carefully navigated during surgery.

Assessment of structural disconnections in gliomas: comparison of indirect and direct approaches.

Gliomas are amongst the most common primary brain tumours in adults and are often associated with poor prognosis. Understanding the extent of white matter (WM) which is affected outside the tumoral lesion may be of paramount importance to explain cognitive deficits and the clinical progression of the disease. To this end, we explored both direct (i.e., tractography based) and indirect (i.e., atlas-based) approaches to quantifying WM structural disconnections in a cohort of 44 high- and low-grade glioma patients. While these methodologies have recently gained popularity in the context of stroke and other pathologies, to our knowledge, this is the first time they are applied in patients with brain tumours. More specifically, in this work, we present a quantitative comparison of the disconnection maps provided by the two methodologies by applying well-known metrics of spatial similarity, extension, and correlation. Given the important role the oedematous tissue plays in the physiopathology of tumours, we performed these analyses both by including and excluding it in the definition of the tumoral lesion. This was done to investigate possible differences determined by this choice. We found that direct and indirect approaches offer two distinct pictures of structural disconnections in patients affected by brain gliomas, presenting key differences in several regions of the brain. Following the outcomes of our analysis, we eventually discuss the strengths and pitfalls of these two approaches when applied in this critical field.

Impaired cognitive control in patients with brain tumors.

Though the assessment of cognitive functions is proven to be a reliable prognostic indicator in patients with brain tumors, some of these functions, such as cognitive control, are still rarely investigated. The objective of this study was to examine proactive and reactive control functions in patients with focal brain tumors and to identify lesioned brain areas more at "risk" for developing impairment of these functions. To this end, a group of twenty-two patients, candidate to surgery, were tested with an AX-CPT task and a Stroop task, along with a clinical neuropsychological assessment, and their performance was compared to that of a well-matched healthy control group. Although overall accuracy and response times were similar for patients and control groups, the patient group failed more on the BX trials of the AX-CPT task and on the incongruent trials of the Stroop task, specifically. Behavioral results were associated with the damaged brain areas, mostly distributed in right frontal regions, by means of a lesion-symptom mapping multivariate approach. This analysis showed that a white matter cluster in the right prefrontal area was associated with lower d'-context values on the AX-CPT, which reflected the fact that these patients rely more on later information (reactive processes) to respond to unexpected and conflicting stimuli, than on earlier contextual cues (proactive processes). Taken together, these results suggest that patients with brain tumors present an imbalance between proactive and reactive control strategies in high interfering conditions, in association with right prefrontal white matter lesions.

Diffusion-based microstructure models in brain tumours: Fitting in presence of a model-microstructure mismatch.

Diffusion-based biophysical models have been used in several recent works to study the microenvironment of brain tumours. While the pathophysiological interpretation of the parameters of these models remains unclear, their use as signal representations may yield useful biomarkers for monitoring the treatment and the progression of this complex and heterogeneous disease. Up to now, however, no study was devoted to assessing the mathematical stability of these approaches in cancerous brain regions. To this end, we analyzed in 11 brain tumour patients the fitting results of two microstructure models (Neurite Orientation Dispersion and Density Imaging and the Spherical Mean Technique) and of a signal representation (Diffusion Kurtosis Imaging) to compare the reliability of their parameter estimates in the healthy brain and in the tumoral lesion. The framework of our between-tissue analysis included the computation of 1) the residual sum of squares as a goodness-of-fit measure 2) the standard deviation of the models' derived metrics and 3) models' sensitivity functions to analyze the suitability of the employed protocol for parameter estimation in the different microenvironments. Our results revealed no issues concerning the fitting of the models in the tumoral lesion, with similar goodness of fit and parameter precisions occurring in normal appearing and pathological tissues. Lastly, with the aim of highlight possible biomarkers, in our analysis we briefly discuss the correlation between the metrics of the three techniques, identifying groups of indices which are significantly collinear in all tissues and thus provide no additional information when jointly used in data-driven analyses.

Magnetic Resonance Imaging Correlates of Immune Microenvironment in Glioblastoma.

Background: Glioblastoma (GBM) is the most commonly occurring primary malignant brain tumor, and it carries a dismal prognosis. Focusing on the tumor microenvironment may provide new insights into pathogenesis, but no clinical tools are available to do this. We hypothesized that the infiltration of different leukocyte populations in the tumoral and peritumoral brain tissues may be measured by magnetic resonance imaging (MRI). Methods: Pre-operative MRI was combined with immune phenotyping of intraoperative tumor tissue based on flow cytometry of myeloid cell populations that are associated with immune suppression, namely, microglia and bone marrow-derived macrophages (BMDM). These cell populations were measured from the central and marginal areas of the lesion identified intraoperatively with 5-aminolevulinic acid-guided surgery. MRI features (volume, mean and standard deviation of signal intensity, and fractality) were derived from all MR sequences (T1w, Gd+ T1w, T2w, FLAIR) and ADC MR maps and from different tumor areas (contrast- and non-contrast-enhancing tumor, necrosis, and edema). The principal components of MRI features were correlated with different myeloid cell populations by Pearson's correlation. Results: We analyzed 126 samples from 62 GBM patients. The ratio between BMDM and microglia decreases significantly from the central core to the periphery. Several MRI-derived principal components were significantly correlated (p <0.05, r range: [-0.29, -0.41]) with the BMDM/microglia ratio collected in the central part of the tumor. Conclusions: We report a significant correlation between structural MRI clinical imaging and the ratio of recruited vs. resident macrophages with different immunomodulatory activities. MRI features may represent a novel tool for investigating the microenvironment of GBM.

Presurgical predictors of early cognitive outcome after brain tumor resection in glioma patients.

Gliomas are commonly characterized by neurocognitive deficits that strongly impact patients' and caregivers' quality of life. Surgical resection is the mainstay of therapy, and it can also cause cognitive impairment. An important clinical problem is whether patients who undergo surgery will show post-surgical cognitive impairment above and beyond that present before surgery. The relevant rognostic factors are largely unknown. This study aims to quantify the cognitive impairment in glioma patients 1-week after surgery and to compare different pre-surgical information (i.e., cognitive performance, tumor volume, grading, and lesion topography) towards predicting early post-surgical cognitive outcome. We retrospectively recruited a sample of N = 47 patients affected by high-grade and low-grade glioma undergoing brain surgery for tumor resection. Cognitive performance was assessed before and immediately after (∼1 week) surgery with an extensive neurocognitive battery. Multivariate linear regression models highlighted the combination of predictors that best explained post-surgical cognitive impairment. The impact of surgery on cognitive functioning was relatively small (i.e., 85% of test scores across the whole sample indicated no decline), and pre-operative cognitive performance was the main predictor of early post-surgical cognitive outcome above and beyond information from tumor topography and volume. In fact, structural lesion information did not significantly improve the accuracy of prediction made from cognitive data before surgery. Our findings suggest that post-surgery neurocognitive deficits are only partially explained by preoperative brain damage. The present results suggest the possibility to make reliable, individualized, and clinically relevant predictions from relatively easy-to-obtain information.

Assessing different approaches to estimate single-subject metabolic connectivity from dynamic [18F]fluorodeoxyglucose Positron Emission Tomography data.

Metabolic connectivity is conventionally calculated in terms of correlation of static positron emission tomography (PET) measurements across subjects. There is increasing interest in deriving metabolic connectivity at the single-subject level from dynamic PET data, in a similar way to functional magnetic resonance imaging. However, the strong multicollinearity among region-wise PET time-activity curves (TACs), their non-Gaussian distribution, and the choice of the best strategy for TAC standardization before metabolic connectivity estimation, are non-trivial methodological issues to be tackled.In this work we test four different approaches to estimate sparse inverse covariance matrices, as well as three similarity-based methods to derive adjacency matrices. These approaches, combined with three different TAC standardization strategies, are employed to quantify metabolic connectivity from dynamic [18F]fluorodeoxyglucose ([18F]FDG) PET data in four healthy subjects.