Mario Masellis

Bio: Mario Masellis is an academic researcher from Sunnybrook Health Sciences Centre. The author has contributed to research in topics: Frontotemporal dementia & Dementia. The author has an hindex of 42, co-authored 212 publications receiving 6662 citations. Previous affiliations of Mario Masellis include Sunnybrook Research Institute & Centre for Addiction and Mental Health.

Blood–brain barrier opening in Alzheimer’s disease using MR-guided focused ultrasound

Abstract: Magnetic resonance-guided focused ultrasound in combination with intravenously injected microbubbles has been shown to transiently open the blood–brain barrier, and reduce beta-amyloid and tau pathology in animal models of Alzheimer’s disease. Here, we used focused ultrasound to open the blood–brain barrier in five patients with early to moderate Alzheimer’s disease in a phase I safety trial. In all patients, the blood–brain barrier within the target volume was safely, reversibly, and repeatedly opened. Opening the blood–brain barrier did not result in serious clinical or radiographic adverse events, as well as no clinically significant worsening on cognitive scores at three months compared to baseline. Beta-amyloid levels were measured before treatment using [18F]-florbetaben PET to confirm amyloid deposition at the target site. Exploratory analysis suggested no group-wise changes in amyloid post-sonication. The results of this safety and feasibility study support the continued investigation of focused ultrasound as a potential novel treatment and delivery strategy for patients with Alzheimer’s disease. Magnetic resonance-guided focused ultrasound with injected microbubbles has been used to temporarily open the blood–brain barrier (BBB) in animal models of Alzheimer's disease (AD). Here, the authors use this technology to non-invasively open the BBB in 5 patients with mild-to-moderate AD in a phase I trial, and show that the procedure is safe.

Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis

Abstract: Summary Background Frontotemporal dementia is a highly heritable neurodegenerative disorder. In about a third of patients, the disease is caused by autosomal dominant genetic mutations usually in one of three genes: progranulin ( GRN ), microtubule-associated protein tau ( MAPT ), or chromosome 9 open reading frame 72 ( C9orf72 ). Findings from studies of other genetic dementias have shown neuroimaging and cognitive changes before symptoms onset, and we aimed to identify whether such changes could be shown in frontotemporal dementia. Methods We recruited participants to this multicentre study who either were known carriers of a pathogenic mutation in GRN, MAPT , or C9orf72 , or were at risk of carrying a mutation because a first-degree relative was a known symptomatic carrier. We calculated time to expected onset as the difference between age at assessment and mean age at onset within the family. Participants underwent a standardised clinical assessment and neuropsychological battery. We did MRI and generated cortical and subcortical volumes using a parcellation of the volumetric T1-weighted scan. We used linear mixed-effects models to examine whether the association of neuropsychology and imaging measures with time to expected onset of symptoms differed between mutation carriers and non-carriers. Findings Between Jan 30, 2012, and Sept 15, 2013, we recruited participants from 11 research sites in the UK, Italy, the Netherlands, Sweden, and Canada. We analysed data from 220 participants: 118 mutation carriers (40 symptomatic and 78 asymptomatic) and 102 non-carriers. For neuropsychology measures, we noted the earliest significant differences between mutation carriers and non-carriers 5 years before expected onset, when differences were significant for all measures except for tests of immediate recall and verbal fluency. We noted the largest Z score differences between carriers and non-carriers 5 years before expected onset in tests of naming (Boston Naming Test −0·7; SE 0·3) and executive function (Trail Making Test Part B, Digit Span backwards, and Digit Symbol Task, all −0·5, SE 0·2). For imaging measures, we noted differences earliest for the insula (at 10 years before expected symptom onset, mean volume as a percentage of total intracranial volume was 0·80% in mutation carriers and 0·84% in non-carriers; difference −0·04, SE 0·02) followed by the temporal lobe (at 10 years before expected symptom onset, mean volume as a percentage of total intracranial volume 8·1% in mutation carriers and 8·3% in non-carriers; difference −0·2, SE 0·1). Interpretation Structural imaging and cognitive changes can be identified 5–10 years before expected onset of symptoms in asymptomatic adults at risk of genetic frontotemporal dementia. These findings could help to define biomarkers that can stage presymptomatic disease and track disease progression, which will be important for future therapeutic trials. Funding Centres of Excellence in Neurodegeneration.

A Multicenter Study of Glucocerebrosidase Mutations in Dementia With Lewy Bodies

Abstract: IMPORTANCE: While mutations in glucocerebrosidase (GBA1) are associated with an increased risk for Parkinson disease (PD), it is important to establish whether such mutations are also a common risk factor for other Lewy body disorders. OBJECTIVE: To establish whether GBA1 mutations are a risk factor for dementia with Lewy bodies (DLB). DESIGN We compared genotype data on patients and controls from 11 centers. Data concerning demographics, age at onset, disease duration, and clinical and pathological features were collected when available. We conducted pooled analyses using logistic regression to investigate GBA1 mutation carrier status as predicting DLB or PD with dementia status, using common control subjects as a reference group. Random-effects meta-analyses were conducted to account for additional heterogeneity. SETTING: Eleven centers from sites around the world performing genotyping. PARTICIPANTS: Seven hundred twenty-one cases met diagnostic criteria for DLB and 151 had PD with dementia. We compared these cases with 1962 controls from the same centers matched for age, sex, and ethnicity. MAIN OUTCOME MEASURES: Frequency of GBA1 mutations in cases and controls. RESULTS We found a significant association between GBA1 mutation carrier status and DLB, with an odds ratio of 8.28 (95% CI, 4.78-14.88). The odds ratio for PD with dementia was 6.48 (95% CI, 2.53-15.37). The mean age at diagnosis of DLB was earlier in GBA1 mutation carriers than in noncarriers (63.5 vs 68.9 years; P < .001), with higher disease severity scores. CONCLUSIONS AND RELEVANCE: Mutations in GBA1 are a significant risk factor for DLB. GBA1 mutations likely play an even larger role in the genetic etiology of DLB than in PD, providing insight into the role of glucocerebrosidase in Lewy body disease.

Research criteria for the diagnosis of prodromal dementia with Lewy bodies

Abstract: The prodromal phase of dementia with Lewy bodies (DLB) includes (1) mild cognitive impairment (MCI), (2) delirium-onset, and (3) psychiatric-onset presentations. The purpose of our review is to determine whether there is sufficient information yet available to justify development of diagnostic criteria for each of these. Our goal is to achieve evidence-based recommendations for the recognition of DLB at a predementia, symptomatic stage. We propose operationalized diagnostic criteria for probable and possible mild cognitive impairment with Lewy bodies, which are intended for use in research settings pending validation for use in clinical practice. They are compatible with current criteria for other prodromal neurodegenerative disorders including Alzheimer and Parkinson disease. Although there is still insufficient evidence to propose formal criteria for delirium-onset and psychiatric-onset presentations of DLB, we feel that it is important to characterize them, raising the index of diagnostic suspicion and prioritizing them for further investigation.

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

Abstract: The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique-Subtype and Stage Inference (SuStaIn)-able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer's disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 × 10-4) or temporal stage (p = 3.96 × 10-5). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.

Using Multivariate Statistics

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Statistical Parametric Mapping The Analysis Of Functional Brain Images

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Pharmacogenomics — Drug Disposition, Drug Targets, and Side Effects

Abstract: It is well recognized that different patients respond in different ways to the same medication. These differences are often greater among members of a population than they are within the same person at different times (or between monozygotic twins).1 The existence of large population differences with small intrapatient variability is consistent with inheritance as a determinant of drug response; it is estimated that genetics can account for 20 to 95 percent of variability in drug disposition and effects.2 Although many nongenetic factors influence the effects of medications, including age, organ function, concomitant therapy, drug interactions, and the nature of the . . .