Gloria Castellazzi
@ucl.ac.uk
Neuroinflammation
UCL Institute of Neurology
RESEARCH INTERESTS
functional MRI
dynamic functional MRI
perfusion MRI
diffusion MRI
quantitative MRI
signal processing
machine learning
deep learning
Scopus Publications
Scopus Publications
Daniele Martinelli, Maria Magdalena Pocora, Roberto De Icco, Marta Allena, Gloria Vaghi, Grazia Sances, Gloria Castellazzi, and Cristina Tassorelli
MDPI AG
OnabotulinumtoxinA (BonT-A) reduces migraine frequency in a considerable portion of patients with migraine. So far, predictive characteristics of response are lacking. Here, we applied machine learning (ML) algorithms to identify clinical characteristics able to predict treatment response. We collected demographic and clinical data of patients with chronic migraine (CM) or high-frequency episodic migraine (HFEM) treated with BoNT-A at our clinic in the last 5 years. Patients received BoNT-A according to the PREEMPT (Phase III Research Evaluating Migraine Prophylaxis Therapy) paradigm and were classified according to the monthly migraine days reduction in the 12 weeks after the fourth BoNT-A cycle, as compared to baseline. Data were used as input features to run ML algorithms. Of the 212 patients enrolled, 35 qualified as excellent responders to BoNT-A administration and 38 as nonresponders. None of the anamnestic characteristics were able to discriminate responders from nonresponders in the CM group. Nevertheless, a pattern of four features (age at onset of migraine, opioid use, anxiety subscore at the hospital anxiety and depression scale (HADS-a) and Migraine Disability Assessment (MIDAS) score correctly predicted response in HFEM. Our findings suggest that routine anamnestic features acquired in real-life settings cannot accurately predict BoNT-A response in migraine and call for a more complex modality of patient profiling.
Jed Wingrove, Janine Makaronidis, Ferran Prados, Baris Kanber, Marios C. Yiannakas, Cormac Magee, Gloria Castellazzi, Louis Grandjean, Xavier Golay, Carmen Tur,et al.
Elsevier BV
Daniele Martinelli, Sebastiano Arceri, Roberto De Icco, Marta Allena, Elena Guaschino, Natascia Ghiotto, Vito Bitetto, Gloria Castellazzi, Giuseppe Cosentino, Grazia Sances,et al.
SAGE Publications
Introduction In this open label, single-arm trial we evaluated the efficacy of onabotulinum toxin-A in the prevention of high-frequency episodic migraine (8–14 migraine days/month). Methods We enrolled 32 high-frequency episodic migraine subjects (age 44.8 ± 11.9 years, 11.0 ± 2.2 migraine days, 11.5 ± 2.1 headache days, 7 females). After a 28-day baseline period, subjects underwent 4 subsequent onabotulinum toxin-A treatments according to the phase III research evaluating migraine prophylaxis therapy (PREEMPT) paradigm, 12-weeks apart. The primary outcome was the reduction of monthly migraine days from baseline in the 12-week period following the last onabotulinum toxin-A treatment Results Onabotulinum toxin-A reduced monthly migraine days by 3.68 days (−33.1%, p < 0.01). Thirty-nine percent of the patients experienced a ≥50% reduction in monthly migraine days. Onabotulinum toxin-A also reduced the number of headache days (−33.9%, p < 0.01) and the intake of acute medications (−22.9%, p = 0.03). Disability and quality of life (QoL) scores improved markedly (migraine disability assessment (MIDAS) −41.7%; migraine specific questionnaire (MSQ) −31.7%, p < 0.01). Conclusions The findings suggest that, when administered according to the PREEMPT paradigm, onabotulinum toxin-A is effective in the prevention of high-frequency episodic migraine. Trial Registration: NCT04578782
Danka Jandric, Ilona Lipp, David Paling, David Rog, Gloria Castellazzi, Hamied Haroon, Laura Parkes, Geoff J.M. Parker, Valentina Tomassini, and Nils Muhlert
Ovid Technologies (Wolters Kluwer Health)
Background and ObjectivesCognitive impairment in multiple sclerosis (MS) is associated with functional connectivity abnormalities. While there have been calls to use functional connectivity measures as biomarkers, there remains to be a full understanding of why they are affected in MS. In this cross-sectional study, we tested the hypothesis that functional network regions may be susceptible to disease-related “wear and tear” and that this can be observable on co-occurring abnormalities on other magnetic resonance metrics. We tested whether functional connectivity abnormalities in cognitively impaired patients with MS co-occur with (1) overlapping, (2) local, or (3) distal changes in anatomic connectivity and cerebral blood flow abnormalities.MethodsMultimodal 3T MRI and assessment with the Brief Repeatable Battery of Neuropsychological tests were performed in 102 patients with relapsing-remitting MS and 27 healthy controls. Patients with MS were classified as cognitively impaired if they scored ≥1.5 SDs below the control mean on ≥2 tests (n = 55) or as cognitively preserved (n = 47). Functional connectivity was assessed with Independent Component Analysis and dual regression of resting-state fMRI images. Cerebral blood flow maps were estimated, and anatomic connectivity was assessed with anatomic connectivity mapping and fractional anisotropy of diffusion-weighted MRI. Changes in cerebral blood flow and anatomic connectivity were assessed within resting-state networks that showed functional connectivity abnormalities in cognitively impaired patients with MS.ResultsFunctional connectivity was significantly decreased in the anterior and posterior default mode networks and significantly increased in the right and left frontoparietal networks in cognitively impaired relative to cognitively preserved patients with MS (threshold-free cluster enhancement corrected at p ≤ 0.05, 2 sided). Networks showing functional abnormalities showed altered cerebral blood flow and anatomic connectivity locally and distally but not in overlapping locations.DiscussionWe provide the first evidence that functional connectivity abnormalities are accompanied by local cerebral blood flow and structural connectivity abnormalities but also demonstrate that these effects do not occur in exactly the same location. Our findings suggest a possibly shared pathologic mechanism for altered functional connectivity in brain networks in MS.
Daniele Martinelli, Gloria Castellazzi, Roberto De Icco, Ana Bacila, Marta Allena, Arianna Faggioli, Grazia Sances, Anna Pichiecchio, David Borsook, Claudia A. M. Gandini Wheeler-Kingshott,et al.
MDPI AG
In this study we used nitroglycerin (NTG)-induced migraine attacks as a translational human disease model. Static and dynamic functional connectivity (FC) analyses were applied to study the associated functional brain changes. A spontaneous migraine-like attack was induced in five episodic migraine (EM) patients using a NTG challenge. Four task-free functional magnetic resonance imaging (fMRI) scans were acquired over the study: baseline, prodromal, full-blown, and recovery. Seed-based correlation analysis (SCA) was applied to fMRI data to assess static FC changes between the thalamus and the rest of the brain. Wavelet coherence analysis (WCA) was applied to test time-varying phase-coherence changes between the thalamus and salience networks (SNs). SCA results showed significantly FC changes between the right thalamus and areas involved in the pain circuits (insula, pons, cerebellum) during the prodromal phase, reaching its maximal alteration during the full-blown phase. WCA showed instead a loss of synchronisation between thalami and SN, mainly occurring during the prodrome and full-blown phases. These findings further support the idea that a temporal change in thalamic function occurs over the experimentally induced phases of NTG-induced headache in migraine patients. Correlation of FC changes with true clinical phases in spontaneous migraine would validate the utility of this model.
Beatrice Pizzarotti, Fulvia Palesi, Paolo Vitali, Gloria Castellazzi, Nicoletta Anzalone, Elena Alvisi, Daniele Martinelli, Sara Bernini, Matteo Cotta Ramusino, Mauro Ceroni,et al.
Frontiers Media SA
BackgroundFrontotemporal Spectrum Disorder (FTSD) and Amyotrophic Lateral Sclerosis (ALS) are neurodegenerative diseases often considered as a continuum from clinical, epidemiologic, and genetic perspectives. We used localized brain volume alterations to evaluate common and specific features of FTSD, FTSD-ALS, and ALS patients to further understand this clinical continuum.MethodsWe used voxel-based morphometry on structural magnetic resonance images to localize volume alterations in group comparisons: patients (20 FTSD, seven FTSD-ALS, and 18 ALS) versus healthy controls (39 CTR), and patient groups between themselves. We used mean whole-brain cortical thickness (CT¯) to assess whether its correlations with local brain volume could propose mechanistic explanations of the heterogeneous clinical presentations. We also assessed whether volume reduction can explain cognitive impairment, measured with frontal assessment battery, verbal fluency, and semantic fluency.ResultsCommon (mainly frontal) and specific areas with reduced volume were detected between FTSD, FTSD-ALS, and ALS patients, confirming suggestions of a clinical continuum, while at the same time defining morphological specificities for each clinical group (e.g., a difference of cerebral and cerebellar involvement between FTSD and ALS). CT¯ values suggested extensive network disruption in the pathological process, with indications of a correlation between cerebral and cerebellar volumes and CT¯ in ALS. The analysis of the neuropsychological scores indeed pointed toward an important role for the cerebellum, along with fronto-temporal areas, in explaining impairment of executive, and linguistic functions.ConclusionWe identified common elements that explain the FTSD-ALS clinical continuum, while also identifying specificities of each group, partially explained by different cerebral and cerebellar involvement.
Alberto Redolfi, Silvia De Francesco, Fulvia Palesi, Samantha Galluzzi, Cristina Muscio, Gloria Castellazzi, Pietro Tiraboschi, Giovanni Savini, Anna Nigri, Gabriella Bottini,et al.
Frontiers Media SA
Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify—CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from “slight” to “significant” in 80% of the cases. Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology.
Gloria Castellazzi, Maria Giovanna Cuzzoni, Matteo Cotta Ramusino, Daniele Martinelli, Federica Denaro, Antonio Ricciardi, Paolo Vitali, Nicoletta Anzalone, Sara Bernini, Fulvia Palesi,et al.
Frontiers Media SA
Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD–AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a “mixed VD–AD dementia” (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations.
Roberta Maria Lorenzi, Fulvia Palesi, Gloria Castellazzi, Paolo Vitali, Nicoletta Anzalone, Sara Bernini, Matteo Cotta Ramusino, Elena Sinforiani, Giuseppe Micieli, Alfredo Costa,et al.
Frontiers Media SA
Objective: Brain atrophy is an established biomarker for dementia, yet spinal cord involvement has not been investigated to date. As the spinal cord is relaying sensorimotor control signals from the cortex to the peripheral nervous system and vice-versa, it is indeed a very interesting question to assess whether it is affected by atrophy due to a disease that is known for its involvement of cognitive domains first and foremost, with motor symptoms being clinically assessed too. We, therefore, hypothesize that in Alzheimer’s disease (AD), severe atrophy can affect the spinal cord too and that spinal cord atrophy is indeed an important in vivo imaging biomarker contributing to understanding neurodegeneration associated with dementia. Methods: 3DT1 images of 31 AD and 35 healthy control (HC) subjects were processed to calculate volume of brain structures and cross-sectional area (CSA) and volume (CSV) of the cervical cord [per vertebra as well as the C2-C3 pair (CSA23 and CSV23)]. Correlated features (ρ > 0.7) were removed, and the best subset identified for patients’ classification with the Random Forest algorithm. General linear model regression was used to find significant differences between groups (p ≤ 0.05). Linear regression was implemented to assess the explained variance of the Mini-Mental State Examination (MMSE) score as a dependent variable with the best features as predictors. Results: Spinal cord features were significantly reduced in AD, independently of brain volumes. Patients classification reached 76% accuracy when including CSA23 together with volumes of hippocampi, left amygdala, white and gray matter, with 74% sensitivity and 78% specificity. CSA23 alone explained 13% of MMSE variance. Discussion: Our findings reveal that C2-C3 spinal cord atrophy contributes to discriminate AD from HC, together with more established features. The results show that CSA23, calculated from the same 3DT1 scan as all other brain volumes (including right and left hippocampi), has a considerable weight in classification tasks warranting further investigations. Together with recent studies revealing that AD atrophy is spread beyond the temporal lobes, our result adds the spinal cord to a number of unsuspected regions involved in the disease. Interestingly, spinal cord atrophy explains also cognitive scores, which could significantly impact how we model sensorimotor control in degenerative diseases with a primary cognitive domain involvement. Prospective studies should be purposely designed to understand the mechanisms of atrophy and the role of the spinal cord in AD.
Letizia Casiraghi, Adnan A S Alahmadi, Anita Monteverdi, Fulvia Palesi, Gloria Castellazzi, Giovanni Savini, Karl Friston, Claudia A M Gandini Wheeler-Kingshott, and Egidio D’Angelo
Oxford University Press (OUP)
Abstract Action observation (AO) is crucial for motor planning, imitation learning, and social interaction, but it is not clear whether and how an action execution–observation network (AEON) processes the effort of others engaged in performing actions. In this functional magnetic resonance imaging (fMRI) study, we used a “squeeze ball” task involving different grip forces to investigate whether AEON activation showed similar patterns when executing the task or observing others performing it. Both in action execution, AE (subjects performed the visuomotor task) and action observation, AO (subjects watched a video of the task being performed by someone else), the fMRI signal was detected in cerebral and cerebellar regions. These responses showed various relationships with force mapping onto specific areas of the sensorimotor and cognitive systems. Conjunction analysis of AE and AO was repeated for the “0th” order and linear and nonlinear responses, and revealed multiple AEON nodes remapping the detection of actions, and also effort, of another person onto the observer’s own cerebrocerebellar system. This result implies that the AEON exploits the cerebellum, which is known to process sensorimotor predictions and simulations, performing an internal assessment of forces and integrating information into high-level schemes, providing a crucial substrate for action imitation.
Giovanni Savini, Matteo Pardini, Gloria Castellazzi, Alessandro Lascialfari, Declan Chard, Egidio D’Angelo, and Claudia A. M. Gandini Wheeler-Kingshott
Frontiers Media SA
Cognitive impairment affects about 50% of multiple sclerosis (MS) patients, but the mechanisms underlying this remain unclear. The default mode network (DMN) has been linked with cognition, but in MS its role is still poorly understood. Moreover, within an extended DMN network including the cerebellum (CBL-DMN), the contribution of cortico-cerebellar connectivity to MS cognitive performance remains unexplored. The present study investigated associations of DMN and CBL-DMN structural connectivity with cognitive processing speed in MS, in both cognitively impaired (CIMS) and cognitively preserved (CPMS) MS patients. 68 MS patients and 22 healthy controls (HCs) completed a symbol digit modalities test (SDMT) and had 3T brain magnetic resonance imaging (MRI) scans that included a diffusion weighted imaging protocol. DMN and CBL-DMN tracts were reconstructed with probabilistic tractography. These networks (DMN and CBL-DMN) and the cortico-cerebellar tracts alone were modeled using a graph theoretical approach with fractional anisotropy (FA) as the weighting factor. Brain parenchymal fraction (BPF) was also calculated. In CIMS SDMT scores strongly correlated with the FA-weighted global efficiency (GE) of the network [GE(CBL-DMN): ρ = 0.87, R2 = 0.76, p < 0.001; GE(DMN): ρ = 0.82, R2 = 0.67, p < 0.001; GE(CBL): ρ = 0.80, R2 = 0.64, p < 0.001]. In CPMS the correlation between these measures was significantly lower [GE(CBL-DMN): ρ = 0.51, R2 = 0.26, p < 0.001; GE(DMN): ρ = 0.48, R2 = 0.23, p = 0.001; GE(CBL): ρ = 0.52, R2 = 0.27, p < 0.001] and SDMT scores correlated most with BPF (ρ = 0.57, R2 = 0.33, p < 0.001). In a multivariable regression model where SDMT was the independent variable, FA-weighted GE was the only significant explanatory variable in CIMS, while in CPMS BPF and expanded disability status scale were significant. No significant correlation was found in HC between SDMT scores, MRI or network measures. DMN structural GE is related to cognitive performance in MS, and results of CBL-DMN suggest that the cerebellum structural connectivity to the DMN plays an important role in information processing speed decline.
Claudia A. M. Gandini Wheeler-Kingshott, Frank Riemer, Fulvia Palesi, Antonio Ricciardi, Gloria Castellazzi, Xavier Golay, Ferran Prados, Bhavana Solanky, and Egidio U. D’Angelo
Frontiers Media SA
Brain function has been investigated via the blood oxygenation level dependent (BOLD) effect using magnetic resonance imaging (MRI) for the past decades. Advances in sodium imaging offer the unique chance to access signal changes directly linked to sodium ions (23Na) flux across the cell membrane, which generates action potentials, hence signal transmission in the brain. During this process 23Na transiently accumulates in the intracellular space. Here we show that quantitative functional sodium imaging (fNaI) at 3T is potentially sensitive to 23Na concentration changes during finger tapping, which can be quantified in gray and white matter regions key to motor function. For the first time, we measured a 23Na concentration change of 0.54 mmol/l in the ipsilateral cerebellum, 0.46 mmol/l in the contralateral primary motor cortex (M1), 0.27 mmol/l in the corpus callosum and -11 mmol/l in the ipsilateral M1, suggesting that fNaI is sensitive to distributed functional alterations. Open issues persist on the role of the glymphatic system in maintaining 23Na homeostasis, the role of excitation and inhibition as well as volume distributions during neuronal activity. Haemodynamic and physiological signal recordings coupled to realistic models of tissue function will be critical to understand the mechanisms of such changes and contribute to meeting the overarching challenge of measuring neuronal activity in vivo.
Gloria Castellazzi, Stefania D. Bruno, Ahmed T. Toosy, Letizia Casiraghi, Fulvia Palesi, Giovanni Savini, Egidio D’Angelo, and Claudia Angela Michela Gandini Wheeler-Kingshott
Frontiers Media SA
While task-dependent responses of specific brain areas during cognitive tasks are well established, much less is known about the changes occurring in resting state networks (RSNs) in relation to continuous cognitive processing. In particular, the functional involvement of cerebro-cerebellar loops connecting the posterior cerebellum to associative cortices, remains unclear. In this study, 22 healthy volunteers underwent a multi-session functional magnetic resonance imaging (fMRI) protocol composed of four consecutive 8-min resting state fMRI (rs-fMRI) scans. After a first control scan, participants listened to a narrated story for the entire duration of the second rs-fMRI scan; two further rs-fMRI scans followed the end of story listening. The story plot was purposely designed to stimulate specific cognitive processes that are known to involve the cerebro-cerebellar loops. Almost all of the identified 15 RSNs showed changes in functional connectivity (FC) during and for several minutes after the story. The FC changes mainly occurred in the frontal and prefrontal cortices and in the posterior cerebellum, especially in Crus I-II and lobule VI. The FC changes occurred in cerebellar clusters belonging to different RSNs, including the cerebellar network (CBLN), sensory networks (lateral visual network, LVN; medial visual network, MVN) and cognitive networks (default mode network, DMN; executive control network, ECN; right and left ventral attention networks, RVAN and LVAN; salience network, SN; language network, LN; and working memory network, WMN). Interestingly, a k-means analysis of FC changes revealed clustering of FCN, ECN, and WMN, which are all involved in working memory functions, CBLN, DMN, and SN, which play a key-role in attention switching, and RSNs involved in visual imagery. These results show that the cerebellum is deeply entrained in well-structured network clusters, which reflect multiple aspects of cognitive processing, during and beyond the conclusion of auditory stimulation.
Gloria Castellazzi, Laetitia Debernard, Tracy R. Melzer, John C. Dalrymple-Alford, Egidio D'Angelo, David H. Miller, Claudia A. M. Gandini Wheeler-Kingshott, and Deborah F. Mason
Frontiers Media SA
Resting state functional MRI (rs-fMRI) has provided important insights into functional reorganization in subjects with Multiple Sclerosis (MS) at different stage of disease. In this cross-sectional study we first assessed, by means of rs-fMRI, the impact of overall T2 lesion load (T2LL) and MS severity score (MSSS) on resting state networks (RSNs) in 62 relapsing remitting MS (RRMS) patients with mild disability (MSSS < 3). Independent Component Analysis (ICA) followed by dual regression analysis confirmed functional connectivity (FC) alterations of many RSNs in RRMS subjects compared to healthy controls. The anterior default mode network (DMNa) and the superior precuneus network (PNsup) showed the largest areas of decreased FC, while the sensory motor networks area M1 (SMNm1) and the medial visual network (MVN) showed the largest areas of increased FC. In order to better understand the nature of these alterations as well as the mechanisms of functional alterations in MS we proposed a method, based on linear regression, that takes into account FC changes and their correlation with T2LL and MSSS. Depending on the sign of the correlation between FC and T2LL, and furthermore the sign of the correlation with MSSS, we suggested the following possible underlying mechanisms to interpret altered FC: (1) FC reduction driven by MS lesions, (2) “true” functional compensatory mechanism, (3a) functional compensation attempt, (3b) “false” functional compensation, (4a) neurodegeneration, (4b) pre-symptomatic condition (damage precedes MS clinical manifestation). Our data shows areas satisfying 4 of these 6 conditions (i.e., 1,2,3b,4b), supporting the suggestion that increased FC has a complex nature that may exceed the simplistic assumption of an underlying compensatory mechanism attempting to limit the brain damage caused by MS progression. Exploring differences between RRMS subjects with short disease duration (MSshort) and RRMS with similar disability but longer disease duration (MSlong), we found that MSshort and MSlong were characterized by clearly distinct pattern of FC, involving predominantly sensory and cognitive networks respectively. Overall, these results suggest that the analysis of FC alterations in multiple large-scale networks in relation to radiological (T2LL) and clinical (MSSS, disease duration) status may provide new insights into the pathophysiology of relapse onset MS evolution.
Fulvia Palesi, Andrea De Rinaldis, Paolo Vitali, Gloria Castellazzi, Letizia Casiraghi, Giancarlo Germani, Sara Bernini, Nicoletta Anzalone, Matteo Cotta Ramusino, Federica M. Denaro,et al.
Frontiers Media SA
Alzheimer disease (AD) and vascular dementia (VaD) together represent the majority of dementia cases. Since their neuropsychological profiles often overlap and white matter lesions are observed in elderly subjects including AD, differentiating between VaD and AD can be difficult. Characterization of these different forms of dementia would benefit by identification of quantitative imaging biomarkers specifically sensitive to AD or VaD. Parameters of microstructural abnormalities derived from diffusion tensor imaging (DTI) have been reported to be helpful in differentiating between dementias, but only few studies have used them to compare AD and VaD with a voxelwise approach. Therefore, in this study a whole brain statistical analysis was performed on DTI data of 93 subjects (31 AD, 27 VaD, and 35 healthy controls—HC) to identify specific white matter patterns of alteration in patients affected by VaD and AD with respect to HC. Parahippocampal tracts were found to be mainly affected in AD, while VaD showed more spread white matter damages associated with thalamic radiations involvement. The genu of the corpus callosum was predominantly affected in VaD, while the splenium was predominantly affected in AD revealing the existence of specific patterns of alteration useful in distinguishing between VaD and AD. Therefore, DTI parameters of these regions could be informative to understand the pathogenesis and support the etiological diagnosis of dementia. Further studies on larger cohorts of subjects, characterized for brain amyloidosis, will allow to confirm and to integrate the present findings and, furthermore, to elucidate the mechanisms of mixed dementia. These steps will be essential to translate these advances to clinical practice.
Fulvia Palesi, Andrea De Rinaldis, Gloria Castellazzi, Fernando Calamante, Nils Muhlert, Declan Chard, J. Donald Tournier, Giovanni Magenes, Egidio D’Angelo, and Claudia A. M. Gandini Wheeler-Kingshott
Springer Science and Business Media LLC
Cerebellar involvement in cognition, as well as in sensorimotor control, is increasingly recognized and is thought to depend on connections with the cerebral cortex. Anatomical investigations in animals and post-mortem humans have established that cerebro-cerebellar connections are contralateral to each other and include the cerebello-thalamo-cortical (CTC) and cortico-ponto-cerebellar (CPC) pathways. CTC and CPC characterization in humans in vivo is still challenging. Here advanced tractography was combined with quantitative indices to compare CPC to CTC pathways in healthy subjects. Differently to previous studies, our findings reveal that cerebellar cognitive areas are reached by the largest proportion of the reconstructed CPC, supporting the hypothesis that a CTC-CPC loop provides a substrate for cerebro-cerebellar communication during cognitive processing. Amongst the cerebral areas identified using in vivo tractography, in addition to the cerebral motor cortex, major portions of CPC streamlines leave the prefrontal and temporal cortices. These findings are useful since provide MRI-based indications of possible subtending connectivity and, if confirmed, they are going to be a milestone for instructing computational models of brain function. These results, together with further multi-modal investigations, are warranted to provide important cues on how the cerebro-cerebellar loops operate and on how pathologies involving cerebro-cerebellar connectivity are generated.
Giulia Matrone, Alessandro Ramalli, Alessandro Stuart Savoia, Fabio Quaglia, Gloria Castellazzi, Patrizia Morbini, and Marco Piastra
MyJove Corporation
The possibility to perform an early and repeatable assessment of imaging performance is fundamental in the design and development process of new ultrasound (US) probes. Particularly, a more realistic analysis with application-specific imaging targets can be extremely valuable to assess the expected performance of US probes in their potential clinical field of application. The experimental protocol presented in this work was purposely designed to provide an application-specific assessment procedure for newly-developed US probe prototypes based on Capacitive Micromachined Ultrasonic Transducer (CMUT) technology in relation to brain imaging. The protocol combines the use of a bovine brain fixed in formalin as the imaging target, which ensures both realism and repeatability of the described procedures, and of neuronavigation techniques borrowed from neurosurgery. The US probe is in fact connected to a motion tracking system which acquires position data and enables the superposition of US images to reference Magnetic Resonance (MR) images of the brain. This provides a means for human experts to perform a visual qualitative assessment of the US probe imaging performance and to compare acquisitions made with different probes. Moreover, the protocol relies on the use of a complete and open research and development system for US image acquisition, i.e. the Ultrasound Advanced Open Platform (ULA-OP) scanner. The manuscript describes in detail the instruments and procedures involved in the protocol, in particular for the calibration, image acquisition and registration of US and MR images. The obtained results prove the effectiveness of the overall protocol presented, which is entirely open (within the limits of the instrumentation involved), repeatable, and covers the entire set of acquisition and processing activities for US images.
Fulvia Plaesi
CIC Edizioni Internazionali
The identification of pathways connecting the cerebral cortex with subcortical structures is critical to understanding how large-scale brain networks operate. The cerebellum, for example, is known to project numerous axonal bundles to thecerebral cortex passing through the thalamus. This paper focuses on the technical details of cerebello-thalamo-cortical pathway reconstruction using advanced diffusion MRI techniques in humans in vivo. Pathways reconstructed using seed/target placement on super-resolution maps, created with track density imaging (TDI), were compared with those reconstructed by defining regions of interest (ROIs) on non-diffusion weighted images (b0). We observed that the reconstruction of the pathways was more anatomically accurate when using ROIs placed on TDI rather than on b0 maps, while inter-subject variability and reproducibility were similar between the two methods. Diffusion indices along pathways showed a position-dependent specificity that will need to be taken into consideration in future clinical investigations.
Fulvia Palesi, Gloria Castellazzi, Letizia Casiraghi, Elena Sinforiani, Paolo Vitali, Claudia A. M. Gandini Wheeler-Kingshott, and Egidio D'Angelo
Frontiers Media SA
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a severe derangement of cognitive functions, primarily memory, in elderly subjects. As far as the functional impairment is concerned, growing evidence supports the “disconnection syndrome” hypothesis. Recent investigations using fMRI have revealed a generalized alteration of resting state networks (RSNs) in patients affected by AD and mild cognitive impairment (MCI). However, it was unclear whether the changes in functional connectivity were accompanied by corresponding structural network changes. In this work, we have developed a novel structural/functional connectomic approach: resting state fMRI was used to identify the functional cortical network nodes and diffusion MRI to reconstruct the fiber tracts to give a weight to internodal subcortical connections. Then, local and global efficiency were determined for different networks, exploring specific alterations of integration and segregation patterns in AD and MCI patients compared to healthy controls (HC). In the default mode network (DMN), that was the most affected, axonal loss, and reduced axonal integrity appeared to compromise both local and global efficiency along posterior-anterior connections. In the basal ganglia network (BGN), disruption of white matter integrity implied that main alterations occurred in local microstructure. In the anterior insular network (AIN), neuronal loss probably subtended a compromised communication with the insular cortex. Cognitive performance, evaluated by neuropsychological examinations, revealed a dependency on integration and segregation of brain networks. These findings are indicative of the fact that cognitive deficits in AD could be associated not only with cortical alterations (revealed by fMRI) but also with subcortical alterations (revealed by diffusion MRI) that extend beyond the areas primarily damaged by neurodegeneration, toward the support of an emerging concept of AD as a “disconnection syndrome.” Since only AD but not MCI patients were characterized by a significant decrease in structural connectivity, integrated structural/functional connectomics could provide a useful tool for assessing disease progression from MCI to AD.
Fulvia Palesi, Jacques-Donald Tournier, Fernando Calamante, Nils Muhlert, Gloria Castellazzi, Declan Chard, Egidio D’Angelo, and Claudia A. M. Wheeler-Kingshott
Springer Science and Business Media LLC
In addition to motor functions, it has become clear that in humans the cerebellum plays a significant role in cognition too, through connections with associative areas in the cerebral cortex. Classical anatomy indicates that neo-cerebellar regions are connected with the contralateral cerebral cortex through the dentate nucleus, superior cerebellar peduncle, red nucleus and ventrolateral anterior nucleus of the thalamus. The anatomical existence of these connections has been demonstrated using virus retrograde transport techniques in monkeys and rats ex vivo. In this study, using advanced diffusion MRI tractography we show that it is possible to calculate streamlines to reconstruct the pathway connecting the cerebellar cortex with contralateral cerebral cortex in humans in vivo. Corresponding areas of the cerebellar and cerebral cortex encompassed similar proportion (about 80 %) of the tract, suggesting that the majority of streamlines passing through the superior cerebellar peduncle connect the cerebellar hemispheres through the ventrolateral thalamus with contralateral associative areas. This result demonstrates that this kind of tractography is a useful tool to map connections between the cerebellum and the cerebral cortex and moreover could be used to support specific theories about the abnormal communication along these pathways in cognitive dysfunctions in pathologies ranging from dyslexia to autism.
Mark S. Shiroishi, Gloria Castellazzi, Jerrold L. Boxerman, Francesco D'Amore, Marco Essig, Thanh B. Nguyen, James M. Provenzale, David S. Enterline, Nicoletta Anzalone, Arnd Dörfler,et al.
Wiley
Dynamic susceptibility contrast magnetic resonance imaging (DSC‐MRI) is used to track the first pass of an exogenous, paramagnetic, nondiffusible contrast agent through brain tissue, and has emerged as a powerful tool in the characterization of brain tumor hemodynamics. DSC‐MRI parameters can be helpful in many aspects, including tumor grading, prediction of treatment response, likelihood of malignant transformation, discrimination between tumor recurrence and radiation necrosis, and differentiation between true early progression and pseudoprogression. This review aims to provide a conceptual overview of the underlying principles of DSC‐MRI of the brain for clinical neuroradiologists, scientists, or students wishing to improve their understanding of the technical aspects, pitfalls, and controversies of DSC perfusion MRI of the brain. Future consensus on image acquisition parameters and postprocessing of DSC‐MRI will most likely allow this technique to be evaluated and used in high‐quality multicenter studies and ultimately help guide clinical care. J. Magn. Reson. Imaging 2015;41:296–313.© 2013 Wiley Periodicals, Inc.
Gloria Castellazzi, Fulvia Palesi, Stefano Casali, Paolo Vitali, Elena Sinforiani, Claudia A. M. Wheeler-Kingshott, and Egidio D'Angelo
Frontiers Media SA
In resting state fMRI (rs-fMRI), only functional connectivity (FC) reductions in the default mode network (DMN) are normally reported as a biomarker for Alzheimer's disease (AD). In this investigation we have developed a comprehensive strategy to characterize the FC changes occurring in multiple networks and applied it in a pilot study of subjects with AD and Mild Cognitive Impairment (MCI), compared to healthy controls (HC). Resting state networks (RSNs) were studied in 14 AD (70 ± 6 years), 12 MCI (74 ± 6 years), and 16 HC (69 ± 5 years). RSN alterations were present in almost all the 15 recognized RSNs; overall, 474 voxels presented a reduced FC in MCI and 1244 in AD while 1627 voxels showed an increased FC in MCI and 1711 in AD. The RSNs were then ranked according to the magnitude and extension of FC changes (gFC), putting in evidence 6 RSNs with prominent changes: DMN, frontal cortical network (FCN), lateral visual network (LVN), basal ganglia network (BGN), cerebellar network (CBLN), and the anterior insula network (AIN). Nodes, or hubs, showing alterations common to more than one RSN were mostly localized within the prefrontal cortex and the mesial-temporal cortex. The cerebellum showed a unique behavior where voxels of decreased gFC were only found in AD while a significant gFC increase was only found in MCI. The gFC alterations showed strong correlations (p < 0.001) with psychological scores, in particular Mini-Mental State Examination (MMSE) and attention/memory tasks. In conclusion, this analysis revealed that the DMN was affected by remarkable FC increases, that FC alterations extended over several RSNs, that derangement of functional relationships between multiple areas occurred already in the early stages of dementia. These results warrant future work to verify whether these represent compensatory mechanisms that exploit a pre-existing neural reserve through plasticity, which evolve in a state of lack of connectivity between different networks with the worsening of the pathology.
F. Palesi, P. Vitali, P. Chiarati, G. Castellazzi, E. Caverzasi, A. Pichiecchio, E. Colli-Tibaldi, F. D'Amore, I. D'Errico, E. Sinforiani,et al.
Hindawi Limited
Hippocampal damage, by DTI or MR volumetry, and PET hypoperfusion of precuneus/posterior cingulate cortex (PC/PCC) were proposed as biomarkers of conversion from preclinical (MCI) to clinical stage of Alzheimer's disease (AD). This study evaluated structural damage, by DTI and MR volumetry, of hippocampi and tracts connecting hippocampus to PC/PCC (hipp-PC/PCC) in 10 AD, 10 MCI, and 18 healthy controls (CTRL). Normalized volumes, mean diffusivity (MD), and fractional anisotropy (FA) were obtained for grey matter (GM), white matter (WM), hippocampi, PC/PCC, and hipp-PC/PCC tracts. In hippocampi and hipp-PC/PCC tracts, decreased volumes and increased MD were found in AD versus CTRL (P<.001). The same results with lower significance (P<.05) were found in MCI versus CTRL. Verbal memory correlated (P<.05) in AD with left hippocampal and hipp-PC/PCC tract MD, and in MCI with FA of total WM. Both DTI and MR volumetry of hippocampi and hipp-PC/PCC tracts detect early signs of AD in MCI patients.