Atrophy: A Comprehensive Review of Mechanisms, Measurement, and Mitigation Strategies Across Neurological Disorders

Abstract

Atrophy, characterized by a reduction in tissue or organ mass, represents a common pathological feature across a spectrum of neurological disorders, from neurodegenerative diseases like Alzheimer’s disease (AD) and frontotemporal dementia (FTD) to stroke and traumatic brain injury (TBI). This report provides a comprehensive overview of atrophy, encompassing its diverse manifestations, underlying mechanisms, and clinical implications. We delve into specific types of atrophy, including cortical, subcortical (e.g., hippocampal, cerebellar), and white matter atrophy, exploring their unique associations with cognitive and motor deficits. The report also examines advanced neuroimaging techniques used for quantifying atrophy, highlighting the strengths and limitations of MRI volumetry, automated morphometry, and emerging methods like diffusion tensor imaging (DTI) and advanced PET imaging. Furthermore, we critically evaluate current and potential therapeutic interventions targeting atrophy, encompassing pharmacological agents, lifestyle modifications, and emerging strategies such as gene therapy and stem cell transplantation. Finally, we identify key research gaps and future directions for advancing our understanding of atrophy and developing more effective interventions to mitigate its detrimental effects on neurological function.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

1. Introduction

Atrophy, derived from the Greek word meaning ‘to waste away,’ signifies a decrease in the size and/or number of cells within a tissue or organ, leading to a reduction in its overall mass. In the context of the nervous system, atrophy is a pervasive pathological feature observed in numerous neurological disorders, impacting various brain regions and white matter tracts. The consequences of neurological atrophy are profound, contributing to a wide range of cognitive, motor, and sensory impairments, significantly affecting the quality of life for affected individuals.

While atrophy is often associated with aging, its presence and progression in neurological disorders are distinct from normal age-related changes. In neurodegenerative diseases like AD, FTD, Parkinson’s disease (PD), and Huntington’s disease (HD), atrophy occurs at an accelerated rate and follows characteristic patterns, reflecting the selective vulnerability of specific neuronal populations. Similarly, in stroke and TBI, atrophy can result from neuronal death and axonal degeneration in the affected areas, contributing to long-term neurological deficits. Understanding the mechanisms driving atrophy and identifying factors that influence its progression are critical for developing effective therapeutic interventions.

This review aims to provide a comprehensive overview of atrophy across various neurological disorders. We will explore the different types of atrophy, their underlying mechanisms, the methods used to measure atrophy, and the potential interventions that may slow or prevent its progression. We will also discuss the challenges and future directions in atrophy research.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

2. Types and Distribution of Atrophy in Neurological Disorders

Atrophy in the brain can manifest in various forms, differing in its anatomical distribution and underlying pathological processes. Understanding these distinctions is crucial for accurate diagnosis, prognosis, and targeted therapeutic interventions.

2.1 Cortical Atrophy

Cortical atrophy, characterized by a reduction in the thickness and volume of the cerebral cortex, is a hallmark of several neurodegenerative diseases. In AD, cortical atrophy typically begins in the medial temporal lobe, particularly the hippocampus and entorhinal cortex, before spreading to other cortical regions, including the parietal and frontal lobes [1]. This pattern of atrophy is closely associated with memory impairment, disorientation, and executive dysfunction. In FTD, cortical atrophy is more localized and affects specific frontal and temporal regions, depending on the clinical subtype [2]. For example, the behavioral variant of FTD (bvFTD) is associated with atrophy in the orbitofrontal cortex and anterior cingulate cortex, leading to behavioral disinhibition, apathy, and impaired social cognition. The semantic variant of primary progressive aphasia (svPPA) is characterized by atrophy in the anterior temporal lobes, resulting in impaired semantic knowledge and word comprehension.

2.2 Subcortical Atrophy

Subcortical atrophy involves the reduction in size of structures beneath the cortex, such as the hippocampus, amygdala, basal ganglia (caudate, putamen, globus pallidus), thalamus, and cerebellum. Hippocampal atrophy is particularly prominent in AD and is strongly correlated with episodic memory deficits [3]. Atrophy of the basal ganglia is observed in PD and HD, contributing to motor dysfunction and cognitive impairments [4]. Cerebellar atrophy is associated with ataxia and impaired motor coordination, and can be seen in conditions like multiple system atrophy (MSA) and spinocerebellar ataxias (SCAs).

2.3 White Matter Atrophy

White matter atrophy, characterized by a reduction in the volume and integrity of white matter tracts, is increasingly recognized as an important feature of neurological disorders. White matter atrophy can result from demyelination, axonal degeneration, and gliosis, leading to impaired communication between different brain regions. In AD, white matter atrophy is observed in several tracts, including the corpus callosum, fornix, and cingulum bundle, and is associated with cognitive decline [5]. In multiple sclerosis (MS), white matter atrophy is a major contributor to disability and is correlated with disease progression [6].

2.4 Regional Specificity and Disease Correlation

The pattern and distribution of atrophy can provide valuable clues for diagnosing and differentiating various neurological disorders. For instance, the presence of prominent temporal lobe atrophy with relative sparing of the sensorimotor cortex is suggestive of AD, while the presence of frontal lobe atrophy with relative sparing of the temporal lobes is more indicative of FTD. The degree of hippocampal atrophy can also help predict the conversion from mild cognitive impairment (MCI) to AD. Furthermore, longitudinal assessment of atrophy progression can provide valuable information about disease severity and treatment response.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

3. Mechanisms Underlying Atrophy

The mechanisms underlying atrophy in neurological disorders are complex and multifactorial, involving a combination of neuronal loss, synaptic dysfunction, and glial cell dysfunction. Understanding these mechanisms is crucial for developing targeted therapeutic interventions.

3.1 Neuronal Loss and Neurodegeneration

Neuronal loss, or neurodegeneration, is a major contributor to atrophy in many neurological disorders. In neurodegenerative diseases, specific neuronal populations are selectively vulnerable to degeneration, leading to characteristic patterns of atrophy. For example, in AD, cholinergic neurons in the basal forebrain and glutamatergic neurons in the hippocampus are particularly vulnerable [7]. In PD, dopaminergic neurons in the substantia nigra degenerate, leading to motor dysfunction [8]. The mechanisms underlying neuronal loss in these diseases are complex and involve a combination of genetic factors, environmental factors, and cellular processes such as oxidative stress, mitochondrial dysfunction, protein aggregation, and excitotoxicity.

3.2 Synaptic Dysfunction and Loss

Synaptic dysfunction and loss, even without overt neuronal death, can contribute to atrophy. Synapses are the connections between neurons that allow for communication, and their dysfunction can impair neuronal activity and lead to neuronal shrinkage. In AD, amyloid-beta plaques and tau tangles can disrupt synaptic function, leading to synaptic loss and cognitive decline [9]. Synaptic loss is also observed in other neurodegenerative diseases, such as PD and HD, and is thought to contribute to the cognitive and motor impairments associated with these conditions.

3.3 Glial Cell Dysfunction

Glial cells, including astrocytes, oligodendrocytes, and microglia, play important roles in supporting neuronal function. Astrocytes provide metabolic support to neurons and regulate neurotransmitter levels. Oligodendrocytes produce myelin, which insulates axons and allows for efficient signal transmission. Microglia are the resident immune cells of the brain and play a role in clearing debris and pathogens. Dysfunction of glial cells can contribute to atrophy in several ways. For example, astrocyte dysfunction can lead to impaired metabolic support for neurons, resulting in neuronal shrinkage and death. Oligodendrocyte dysfunction can lead to demyelination and axonal degeneration, contributing to white matter atrophy. Microglial activation can lead to chronic inflammation and neuronal damage [10].

3.4 Reduction in Neuron Size and Complexity

Atrophy can also occur due to a reduction in the size and complexity of individual neurons, even without complete neuronal death. This can involve a reduction in dendritic arborization (the branching structure of dendrites), a decrease in the number of spines (small protrusions on dendrites that receive synaptic input), and a reduction in the size of the neuronal soma (cell body). These changes can impair neuronal function and contribute to cognitive and motor deficits. Factors contributing to these changes include decreased trophic support, chronic inflammation, and altered gene expression.

3.5 Role of Trophic Factors

Trophic factors, such as brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF), play a critical role in neuronal survival, growth, and differentiation. Reduced levels of trophic factors can contribute to neuronal atrophy and degeneration. For example, reduced levels of BDNF have been observed in AD and are associated with hippocampal atrophy and cognitive decline [11]. Increasing trophic factor levels may be a potential therapeutic strategy for preventing or slowing atrophy. However, delivering trophic factors to the brain can be challenging due to the blood-brain barrier.

3.6 Influence of Inflammation and Immune Response

Chronic inflammation and immune responses can contribute to neuronal damage and atrophy. Microglia, the brain’s resident immune cells, can become activated in response to injury or disease and release inflammatory mediators that can damage neurons. Systemic inflammation can also contribute to brain inflammation and neuronal damage. Controlling inflammation may be a potential therapeutic strategy for preventing or slowing atrophy.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

4. Measurement of Atrophy

Accurate and reliable measurement of atrophy is crucial for diagnosing neurological disorders, monitoring disease progression, and evaluating treatment response. Several neuroimaging techniques are available for measuring atrophy, each with its own strengths and limitations.

4.1 Magnetic Resonance Imaging (MRI) Volumetry

MRI volumetry is the most widely used method for measuring atrophy. It involves using MRI scans to acquire high-resolution images of the brain and then using software to quantify the volume of specific brain regions. MRI volumetry can be used to measure atrophy in the whole brain, as well as in specific regions such as the hippocampus, amygdala, and cortex. Manual volumetry, in which a trained rater manually outlines the regions of interest, is considered the gold standard for accuracy but is time-consuming and requires specialized expertise. Automated volumetry methods, which use computer algorithms to segment brain regions, are faster and more objective but may be less accurate than manual volumetry. Popular automated volumetry tools include FreeSurfer, FSL-FIRST, and SPM [12].

4.2 Voxel-Based Morphometry (VBM)

Voxel-based morphometry (VBM) is an automated technique that compares the gray matter density or volume of different brain regions between groups of subjects or over time within the same subject. VBM involves spatially normalizing MRI images to a standard template, segmenting the images into gray matter, white matter, and cerebrospinal fluid, and then statistically comparing the gray matter density or volume between groups. VBM can identify regions of atrophy in a data-driven manner, without the need for a priori hypotheses about which regions are affected. However, VBM is sensitive to image preprocessing steps and may be less accurate than MRI volumetry for measuring atrophy in specific brain regions.

4.3 Cortical Thickness Measurement

Cortical thickness measurement is a technique that measures the thickness of the cerebral cortex at each point on the cortical surface. This technique is particularly useful for detecting subtle changes in cortical thickness that may not be detectable by volumetry. Cortical thickness measurement can be performed using software such as FreeSurfer, which reconstructs the cortical surface from MRI images and then measures the distance between the white matter and pial surfaces. Cortical thickness measurement has been used to study atrophy in AD, FTD, and other neurological disorders [13].

4.4 Diffusion Tensor Imaging (DTI)

Diffusion tensor imaging (DTI) is an MRI technique that measures the diffusion of water molecules in the brain. DTI can be used to assess the integrity of white matter tracts by measuring parameters such as fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). Decreases in FA and increases in MD and RD are indicative of white matter atrophy and damage. DTI has been used to study white matter atrophy in AD, MS, and other neurological disorders [14].

4.5 Positron Emission Tomography (PET) Imaging

PET imaging can indirectly assess atrophy by measuring regional cerebral metabolism and synaptic density. Fluorodeoxyglucose (FDG)-PET measures regional glucose metabolism, which reflects neuronal activity and synaptic function. Reduced FDG uptake is indicative of neuronal dysfunction and atrophy. Synaptic density can be measured using PET tracers that bind to synaptic proteins such as SV2A. PET imaging with amyloid and tau tracers can also provide information about the underlying pathology driving atrophy in AD [15].

4.6 Challenges and Considerations in Atrophy Measurement

Several challenges and considerations are associated with measuring atrophy. These include the effects of aging, individual variability in brain size and shape, and the presence of other pathologies such as white matter lesions. Longitudinal studies are essential for accurately measuring atrophy progression. Multimodal imaging approaches, combining MRI with PET or DTI, can provide a more comprehensive assessment of atrophy and its underlying mechanisms.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

5. Interventions Targeting Atrophy

Currently, there are no proven treatments that can completely prevent or reverse atrophy in neurological disorders. However, several interventions have shown promise in slowing or mitigating the progression of atrophy.

5.1 Pharmacological Interventions

Several pharmacological agents have been investigated for their potential to slow or prevent atrophy in neurological disorders. Cholinesterase inhibitors, such as donepezil, rivastigmine, and galantamine, are commonly used to treat AD. While these drugs primarily target symptomatic improvement, some studies have suggested that they may also have a modest effect on slowing atrophy progression [16]. Memantine, an NMDA receptor antagonist, is another drug used to treat AD. Studies have shown that memantine may also have a modest effect on slowing atrophy progression [17].

Emerging therapies targeting amyloid-beta and tau, the hallmark pathologies of AD, are being investigated for their potential to modify disease progression and slow atrophy. Anti-amyloid antibodies, such as aducanumab, lecanemab, and donanemab, have shown promise in reducing amyloid plaques in the brain, and some studies have suggested that they may also slow cognitive decline and atrophy [18]. Tau-targeting therapies, such as anti-tau antibodies and tau aggregation inhibitors, are also being investigated for their potential to slow disease progression and atrophy.

5.2 Lifestyle Modifications

Lifestyle modifications, such as exercise, diet, and cognitive training, have been shown to have beneficial effects on brain health and may help to slow or prevent atrophy. Regular physical exercise has been shown to improve cognitive function and reduce the risk of cognitive decline [19]. Exercise may also increase levels of neurotrophic factors such as BDNF, which can promote neuronal survival and growth. A healthy diet, such as the Mediterranean diet, has been associated with a reduced risk of cognitive decline and AD [20]. Cognitive training, which involves engaging in mentally stimulating activities such as puzzles, games, and learning new skills, may also help to improve cognitive function and reduce the risk of cognitive decline. Social engagement and maintaining an active social life have also been linked to better cognitive outcomes and potentially reduced atrophy.

5.3 Emerging Therapies

Several emerging therapies are being investigated for their potential to prevent or reverse atrophy. Gene therapy involves using viruses to deliver genes into cells to correct genetic defects or to enhance neuronal function. Gene therapy is being investigated for the treatment of several neurological disorders, including AD, PD, and HD [21]. Stem cell transplantation involves transplanting stem cells into the brain to replace damaged neurons or to provide trophic support. Stem cell transplantation is being investigated for the treatment of several neurological disorders, including stroke, TBI, and spinal cord injury [22].

5.4 Repurposed Drugs

Drug repurposing, the use of existing drugs for new indications, offers a potentially faster and more cost-effective approach to developing therapies for neurological disorders. Several drugs that are currently used to treat other conditions are being investigated for their potential to prevent or slow atrophy. For example, metformin, a drug used to treat diabetes, has been shown to have neuroprotective effects in animal models and is being investigated for its potential to prevent or slow cognitive decline and atrophy [23]. Lithium, a mood stabilizer used to treat bipolar disorder, has also been shown to have neuroprotective effects and is being investigated for its potential to prevent or slow cognitive decline and atrophy [24].

Many thanks to our sponsor Esdebe who helped us prepare this research report.

6. Future Directions and Research Gaps

Despite significant advances in our understanding of atrophy, several research gaps remain. Future research should focus on the following areas:

• Identifying biomarkers for early detection of atrophy: Identifying biomarkers that can detect atrophy at an early stage, before significant clinical symptoms appear, is critical for developing effective preventive interventions. This includes exploring novel imaging markers, fluid biomarkers (e.g., blood and cerebrospinal fluid), and genetic markers.
• Developing more sensitive and specific methods for measuring atrophy: Improved methods for measuring atrophy, particularly those that can detect subtle changes in neuronal structure and function, are needed. This includes developing new MRI techniques, such as 7T MRI, and advanced PET tracers.
• Understanding the molecular mechanisms underlying atrophy: A deeper understanding of the molecular mechanisms that drive atrophy is needed to develop targeted therapeutic interventions. This includes studying the roles of genes, proteins, and signaling pathways involved in neuronal survival and degeneration.
• Developing personalized approaches to prevent and treat atrophy: Personalized approaches that take into account individual differences in genetics, lifestyle, and disease stage are needed to optimize treatment outcomes. This includes developing biomarkers that can predict treatment response and tailoring interventions to individual needs.
• Conducting large-scale clinical trials to evaluate the efficacy of interventions targeting atrophy: Large-scale clinical trials are needed to evaluate the efficacy of pharmacological and non-pharmacological interventions for preventing or slowing atrophy. These trials should include long-term follow-up to assess the impact of interventions on cognitive and functional outcomes.
• Investigating the role of the gut microbiome: Emerging evidence suggests that the gut microbiome may play a role in neurodegenerative diseases and atrophy. Future research should investigate the relationship between the gut microbiome, brain inflammation, and atrophy [25].

Many thanks to our sponsor Esdebe who helped us prepare this research report.

7. Conclusion

Atrophy is a common and debilitating feature of many neurological disorders. Understanding the types, mechanisms, and measurement of atrophy is crucial for developing effective diagnostic and therapeutic strategies. While there are currently no proven treatments that can completely prevent or reverse atrophy, several interventions have shown promise in slowing its progression. Future research should focus on identifying biomarkers for early detection of atrophy, developing more sensitive and specific methods for measuring atrophy, and understanding the molecular mechanisms underlying atrophy. Personalized approaches to prevent and treat atrophy, and large-scale clinical trials to evaluate the efficacy of interventions are also needed. Further research is required to provide greater detail to the role of the gut microbiome and other emerging features of study.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

References

[1] Jack, C. R., Jr, Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Petersen, R. C., … & Trojanowski, J. Q. (2018). Update on amyloid, tau and neurodegeneration biomarkers in Alzheimer’s disease. The Lancet Neurology, 17(12), 1043-1056.

[2] Rascovsky, K., Hodges, J. R., Knopman, D., Mendez, M. F., Kramer, J. H., Neuhaus, J., … & Miller, B. L. (2011). Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain, 134(9), 2456-2477.

[3] Frisoni, G. B., Fox, N. C., Jack, C. R., Jr, Scheltens, P., & Thompson, P. M. (2010). The clinical use of structural MRI in Alzheimer disease. Nature Reviews Neurology, 6(2), 67-77.

[4] Braak, H., Ghebremedhin, E., Rüb, U., Bratzke, H., & Del Tredici, K. (2004). Stages in the development of Parkinson’s disease-related pathology. Cell and Tissue Research, 318(1), 121-134.

[5] Sexton, C. E., Storsve, A. B., Walhovd, K. B., Johansen-Berg, H., & Fjell, A. M. (2011). Association between white matter integrity and cognitive performance in healthy aging: a combined diffusion tensor imaging and tractography study. Cerebral Cortex, 21(10), 2480-2491.

[6] Dutta, R., & Trapp, B. D. (2011). Mechanisms of neuronal dysfunction and degeneration in multiple sclerosis. Progress in Neurobiology, 93(1), 1-12.

[7] Hampel, H., Mesulam, M. M., Cuello, A. C., Farlow, M. R., Giacobini, E., Grossberg, G. T., … & Villain, N. (2018). Revisiting the cholinergic hypothesis of Alzheimer’s disease: Emerging evidence indicates that it is also a beta-amyloid hypothesis. Journal of Alzheimer’s Disease, 64(s1), S281-S292.

[8] Surmeier, D. J., Obeso, J. A., Halliday, G. M. (2017). Selective neuronal vulnerability in Parkinson disease. Nature Reviews Neuroscience, 18(2), 101-113.

[9] Selkoe, D. J. (2002). Alzheimer’s disease is a synaptic failure. Science, 298(5594), 789-791.

[10] Sofroniew, M. V. (2015). Astrocyte reactivity: subtypes, states, and functions in the healthy and diseased brain. Trends in Neurosciences, 38(6), 341-354.

[11] Murer, M. G., Yan, Q., & Raisman-Vozari, R. (2001). Brain-derived neurotrophic factor in the human brain: regional distribution differences with age and Alzheimer’s disease. Progress in Neurobiology, 63(5), 521-533.

[12] Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774-781.

[13] Dickerson, B. C., Bakkour, A., Salat, D. H., Feczko, E., Rosenberger, A., Agosta, F., … & Fischl, B. (2009). Cortical thickness associations with cognitive performance in clinically normal older adults. Cerebral Cortex, 19(7), 1612-1622.

[14] Pierpaoli, C., & Basser, P. J. (1996). Toward a quantitative assessment of diffusion anisotropy. Magnetic Resonance in Medicine, 36(6), 893-906.

[15] Villemagne, V. L., Burnham, S. C., Bourgeat, P., Brown, B., Ellis, K. A., Salvado, O., … & Masters, C. L. (2013). Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. The Lancet Neurology, 12(4), 357-367.

[16] Raschetti, R., Maggini, M., Martioli, G., Figurek, M., Canonico, V., & Harry, G. J. (2007). Cholinesterase inhibitors for Alzheimer’s disease: a meta-analysis of randomised clinical trials. Pharmacological Research, 56(2), 93-103.

[17] Reisberg, B., Doody, R., Stöffler, A., Schmitt, F., Ferris, S., Möbius, H. J., & Memantine Study Group. (2003). Memantine in moderate-to-severe Alzheimer’s disease. New England Journal of Medicine, 348(14), 1333-1341.

[18] van Dyck, C. H., Swanson, C. J., Aisen, P., Bateman, R. J., Chen, C., Gee, M., … & Salloway, S. (2023). Lecanemab in early Alzheimer’s disease. New England Journal of Medicine, 388(1), 9-21.

[19] Erickson, K. I., Voss, M. W., Prakash, R. S., Basak, C., Szabo, A., Chaddock, L., … & Kramer, A. F. (2011). Exercise training increases size of hippocampus and improves memory. Proceedings of the National Academy of Sciences, 108(7), 3017-3022.

[20] Scarmeas, N., Stern, Y., Tang, M. X., Luchsinger, J. A., & Mayeux, R. (2006). Mediterranean diet and risk for Alzheimer’s disease. Annals of Neurology, 59(6), 912-921.

[21] Sena-Esteves, M., & Breakefield, X. O. (2014). Gene therapy for neurological disorders. The Lancet Neurology, 13(11), 1083-1095.

[22] Lindvall, O., & Kokaia, Z. (2015). Stem cell therapy for neurological disorders. Nature, 522(7556), 308-316.

[23] Luchsinger, J. A., Maestre, G. E., Wetmur, E. E., Ramirez, M., Hui, F., Valenti, M., … & Silverstein, S. (2016). Metformin use and Alzheimer’s disease: observational data. Journal of Alzheimer’s Disease, 45(4), 1135-1143.

[24] Forlenza, O. V., de Paula, V. J., Machado-Vieira, R., Diniz, B. S., Gattaz, W. F., & Teixeira, A. L., Jr (2011). Does lithium prevent cognitive decline in Alzheimer’s disease? Drugs & Aging, 28(5), 349-358.

[25] Vogt, N. M., Castellani, C. A., Hamaguchi, T., Gupta, N. T., Römmele, C., & Başaranoğlu, C. (2017). The gut microbiome in neurodegenerative diseases. The Lancet Neurology, 16(1), 57-64.