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Jellinger KA. The enigma of depression in corticobasal degeneration, a frequent but poorly understood co-morbidity. J Neural Transm (Vienna) 2024; 131:195-202. [PMID: 38216704 DOI: 10.1007/s00702-023-02731-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 12/15/2023] [Indexed: 01/14/2024]
Abstract
Depression is one of the most frequent neuropsychiatric symptoms in corticobasal degeneration (CBD), a rare, sporadic, and late-onset progressive neurodegenerative disorder of unknown etiology. It is clinically characterized by a levodopa-poorly responsible akinetic-rigid syndrome, apraxia, limb dystonia, cognitive, mood, behavioral, and language disorders. This 4-repeat (4R) tauopathy is morphologically featured by asymmetric frontoparietal atrophy, neuronal loss, and gliosis in cortex and subcortex including substantia nigra, ballooned/achromatic neurons with filamentous 4R tau aggregates in cortex and striatum, widespread thread-like structures, pathognomonic "astroglial plaques", "tufted astrocytes", and numerous "coiled bodies" (in astrocytes and oligodendroglia) in cerebral white matter. CBD is non-specific, as pathologically proven cases include several clinical phenotypes. Pubmed and Google Scholar were systematically analyzed until October 2023, with focus on the prevalence, clinical manifestation, neuroimaging data, and treatment options of depression in CBD. Its prevalence is about 30-40% which is more frequent than in most other atypical parkinsonian syndromes. Depression usually does not correlate with motor and other clinical parameters, suggesting different pathophysiological mechanisms. Asymmetric atrophy and hypometabolism of frontoparietal cortical areas are associated with disruption of fronto-subcortical circuits, nigrostriatal dopaminergic, and cholinergic deficiency. Since no specific neuroimaging, neuropathological, or biomarker studies of depression in CBD are available, its pathobiological mechanisms and pathogenesis are poorly understood. Antidepressive therapy may be useful, but is often poorly tolerated. Depression in CBD, like in other parkinsonian syndromes, may be related to multi-regional patterns of cerebral disturbances and complex pathogenic mechanisms that deserve further elucidation as a basis for early diagnosis and adequate treatment to improve the quality of life in this fatal disease.
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Affiliation(s)
- Kurt A Jellinger
- Institute of Clinical Neurobiology, Alberichgasse 5/13, 1150, Vienna, Austria.
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Jellinger KA. Pathomechanisms of cognitive and behavioral impairment in corticobasal degeneration. J Neural Transm (Vienna) 2023; 130:1509-1522. [PMID: 37659990 DOI: 10.1007/s00702-023-02691-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 08/23/2023] [Indexed: 09/04/2023]
Abstract
Corticobasal degeneration (CBD) is a rare, sporadic, late-onset progressive neurodegenerative disorder of unknown etiology, clinically characterized by an akinetic-rigid syndrome, behavior and personality disorders, language problems (aphasias), apraxia, executive and cognitive abnormalities and limb dystonia. The syndrome is not specific, as clinical features of pathologically proven CBD include several phenotypes. This 4-repeat (4R) tauopathy is morphologically featured by often asymmetric frontoparietal atrophy, ballooned/achromatic neurons containing filamentous 4R-tau aggregates in cortex and striatum, thread-like processes that are more widespread than in progressive supranuclear palsy (PSP), pathognomonic "astroglial plaques", and numerous inclusions in both astrocytes and oligodendroglia ("coiled bodies") in the white matter. Cognitive deficits in CBD are frequent initial presentations before onset of motor symptoms, depending on the phenotypic variant. They predominantly include executive and visuospatial dysfunction, sleep disorders and language deficits with usually preserved memory domains. Neuroimaging studies showed heterogenous locations of brain atrophy, particularly contralateral to the dominant symptoms, with disruption of striatal connections to prefrontal cortex and basal ganglia circuitry. Asymmetric hypometabolism, mainly involving frontal and parietal regions, is associated with brain cholinergic deficits, and dopaminergic nigrostriatal degeneration. Widespread alteration of cortical and subcortical structures causing heterogenous changes in various brain functional networks support the concept that CBD, similar to PSP, is a brain network disruption disorder. Putative pathogenic factors are hyperphosphorylated tau-pathology, neuroinflammation and oxidative injury, but the basic mechanisms of cognitive impairment in CBD, as in other degenerative movement disorders, are complex and deserve further elucidation as a basis for early diagnosis and adequate treatment of this fatal disorder.
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Affiliation(s)
- Kurt A Jellinger
- Institute of Clinical Neurobiology, Alberichgasse 5/13, 1150, Vienna, Austria.
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Yearley AG, Goedmakers CMW, Panahi A, Doucette J, Rana A, Ranganathan K, Smith TR. FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval. Artif Intell Med 2023; 143:102607. [PMID: 37673576 DOI: 10.1016/j.artmed.2023.102607] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 09/08/2023]
Abstract
Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.
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Affiliation(s)
- Alexander G Yearley
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA.
| | - Caroline M W Goedmakers
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Department of Neurosurgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, Netherlands
| | - Armon Panahi
- The George Washington University School of Medicine and Health Sciences, 2300 I St NW, Washington, DC 20052, USA
| | - Joanne Doucette
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; School of Pharmacy, MCPHS University, 179 Longwood Ave, Boston, MA 02115, USA
| | - Aakanksha Rana
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
| | - Kavitha Ranganathan
- Division of Plastic Surgery, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, USA
| | - Timothy R Smith
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA
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