Corpus callosum atrophy associated with the degree of cognitive decline in patients with Alzheimer's dementia or mild cognitive impairment: A meta-analysis of the region of interest structural imaging studies
Introduction
Alzheimer's disease (AD) is a neurodegenerative disorder with progressive course. Patients of AD commonly present with cognitive and behavioral impairments that markedly interferes with social and occupational functioning. Currently, the diagnosis of AD is mainly clinical, but neuroimaging techniques have become more and more important in the assessment of the elderly with cognitive impairment (Brewer et al., 2013). Computed tomography (CT), volumetric and functional magnetic resonance imaging (MRI), single-photon emission CT (SPECT), or positron emission tomography (PET) have played different roles in the comprehensive evaluation of patients with cognitive impairment. Although the most recent advance of neuroimaging research in Alzheimer's disease has been the development of radioactive tracers targeting proteins potentially involved in the pathogenesis of AD (Rowe et al., 2013, Wong et al., 2010), the structural neuroimaging still plays an important role in assisting clinicians to assess patients with cognitive decline. American Academy of Neurology (AAN) recommended that structural neuroimaging with either a noncontrast CT scan or MRI be appropriate in the initial evaluation of patients with cognitive impairment (Knopman et al., 2001).
MRI allows in vivo visualization of brain structure and disease progression in patients with AD. A number of neuroimaging studies have reported structural brain abnormalities in patients with AD (Bagepally et al., 2013, Clerx et al., 2013, Jack et al., 2008, Tang et al., 2014, van de Pol et al., 2006). Atrophy of the hippocampus on MRI is considered a valid biomarker of AD neuropathology, even though the measurement of hippocampal volume is not used in routine clinical care in the diagnosis of AD. In addition to abnormalities in gray matters, more recent studies have also revealed that AD is also associated with white matter (WM) changes (Teipel et al., 2012, Wang et al., 2012, Zhang et al., 2007).
Corpus callosum (CC), the largest white matter tract in the human brain, plays a key role in the interhemispheric functional integration and communication of perceptual, cognitive, learned, and volitional information (Gazzaniga, 2000, Gazzaniga, 2005). The CC atrophy and structural changes have been found by using the region of interest (ROI) analysis in patients with AD (Di Paola et al., 2010a, Frederiksen et al., 2011, Zhu et al., 2012, Zhu et al., 2014), and some of these studies have revealed that the decrease in the CC midsagittal cross-sectional area was independently associated with the progression of AD. It was speculated that the decrease in the CC area might be due to the loss of callosal fibers (Aboitiz et al., 1992, Riise and Pakkenberg, 2011). Subsequently, functional connectivity between the hemispheres is comprised, which ultimately causes the impairment of cognitive abilities (Ryberg et al., 2011). However, the results from previous studies have been inconsistent. Some studies revealed a smaller CC size in patients with AD compared with healthy controls (Gootjes et al., 2006, Li et al., 2008, Wiltshire et al., 2005); some found a smaller anterior region of the CC (Thomann et al., 2006, Zhu et al., 2012); and others found a smaller posterior the CC (Hanyu et al., 1999, Wang et al., 2006). There were also studies reporting no relationships between AD and total CC size (Kaufer et al., 1997, Thompson et al., 1998). Similarly, the results of callosal changes in structural MRI in patients with mild cognitive impairment (MCI) were also inconsistent (Thomann et al., 2006, Wang et al., 2006).
These data suggest that the CC changes might be associated with cognitive decline in patients with MCI or AD and the inconsistent results might be due to a small sample size of each individual study, the heterogeneity of studied subjects, and different illness stages or methodological differences among studies. Although cortical atrophy plays an important role in AD, the reduction in callosal volumes might be related to the atrophy of the corresponding cerebral cortex (Brun and Englund, 1981). Some studies suggested that damage to myelin may be one of the earliest events in AD (Bartzokis, 2004, Bartzokis et al., 2004), which might be detected with structural imaging techniques. Since CC is the largest white matter and easily to be visualized, the correlation of its volumetric changes with AD or MCI will have large clinical implications. With the combination of radio-tracing technology and structural imaging, the role of CC in AD or MCI will be verified. A meta-analysis of existing studies in CC will provide a platform for future studies in the relationship between subregional CC changes and different stages of AD or MCI.
There are many MRI techniques to quantify structural changes in the human brain (Fu et al., 2014, Guo et al., 2010, Sydykova et al., 2007, Wang et al., 2013, Xie et al., 2006). Generally, callosal microstructural white matter changes can be better studied by a number of techniques such as voxel-based morphometry (VBM) diffusion weighted imaging, and diffusion tensor imaging (DTI). ROI and VBM are the two most popular methods to investigate the structural MRI alteration (Ryan et al., 2013). ROI-based image analysis procedures are often robust, internally consistent, and advisable in some situations (Di Paola et al., 2010b, Glahn et al., 2008). However, VBM analysis is influenced by the amount of residual anatomical variability among subjects after spatial normalization (Good et al., 2001, Lemaitre et al., 2005). Because nonlinear spatial normalization and spatial filtering are used to accommodate anatomical variations among studied brains, there is the danger of missing important differences (Thorns et al., 2013). VBM is also limited by multiple comparisons and poor anatomical definitions (Perlini et al., 2012). Since voxel-based analysis is a method to explore the entire brain, it is more likely to yield false positive findings than ROI analysis (Abe et al., 2010). Some researchers have suggested that the VBM analysis can be used as an exploratory whole-brain approach to identify abnormal brain regions, which should then be validated by using ROI analysis (Giuliani et al., 2005, Honea et al., 2005). Therefore, these two approaches have been considered as complementary rather than competing modalities (Zhang et al., 2010). DTI is sensitive to the degeneration of myelin and axons in the WM microstructure, and ROI is a measure of macroscopic structural characteristics (Wang and Su, 2006). Currently, many researches focus on investigating macroscopic and functional alteration in order to determine their relevance, and to assess their underlying mechanisms (Abe et al., 2010, Dopper et al., 2014, Menke et al., 2014).
From the clinical point of view, ROI analysis is a preferable method in routine clinical practice. It is inexpensive and relatively easy to be conducted although it is a little bit time-consuming compared to VBM and DTI. It does not need powerful processing systems. In addition, because CC is the largest white matter in the brain, its changes can be easily identified and delineated with ROI analysis. (Li et al., 2008). Therefore, a meta-analysis of studies using ROI technique to measure the CC midsagittal area changes in patients with AD or MCI will provide clinically relevant information that may guide clinicians to diagnosis and treatment of patients with AD.
Section snippets
Data source
Databases of PubMed, the Cochrane Library, the ISI Web of Science, and Science Direct from inception up to June 2014 were searched with the following keywords: “corpus callosum” or “callosal”, plus “Alzheimer's disease”, “mild cognitive impairment” or “dementia”. Titles and abstracts were checked to decide whether studies should be included, and then the full text of candidate studies were examined for further determination. In addition, the reference lists of the included articles were also
Characteristics of included studies
A total of 1787 studies were initially identified with our aforementioned search strategy, and 25 of them met our inclusion criteria for AD analysis (Anstey et al., 2007, Biegon et al., 1994, Black et al., 2000, Cuenod et al., 1993, Fazekas et al., 1996, Frederiksen et al., 2011, Gootjes et al., 2006, Hanyu et al., 1999, Hensel et al., 2002, Janowsky et al., 1996, Kaufer et al., 1997, Li et al., 2008, Lyoo et al., 1997, Pantel et al., 1999, Pitel et al., 2010, Teipel et al., 2003, Thomann et
Discussion
This meta-analysis revealed significant CC atrophy in both patients with MCI and those with AD relative to healthy controls, although the results from individual studies were inconsistent. More importantly, the changes in the CC midsagittal area was apparently associated with stages of cognitive decline as reflected by patients with MCI had the smallest reduction in the CC area and patients with moderate AD had the largest reduction compared with healthy controls (Table 3, Fig. 3). In terms of
Limitations
There are several limitations of this meta-analysis. First, although we tried to use fail-safe number to evaluate publication bias in our analysis, the bias from original studies could not be avoided. Second, different studies used different head size correction methods. Although we grouped studies with the same methodology for brain size correction for subgroup meta-analyses (Table 2), the variations among the studies could not be controlled. Third, methodological differences of ROI studies
Conclusion
In this meta-analysis of structural imaging studies using ROI technique and measuring midsagittal area changes of the CC, we found significant midsagittal area reduction in the CC of patients with AD with moderate to large effect sizes relative to healthy controls. The effect sizes were negatively associated with the MMSE scores. In patients with MCI, the reduction of the CC was also significant relative to healthy controls, but with smaller effect sizes compared with patients with AD. In terms
Funding source
This work was in part supported by grant 81171186 form the National Science Foundation of China, grant ZD201417 from the Natural Science Foundation of Heilongjiang Province and fund 20141302 from the First Affiliated Hospital of Harbin Medical University.
Contributors
L. Feng, C. Zhao and Z. Lin designed the study. X. Wang, M. Zhu and W. Gao acquired the data, which X. Wang, M. Ren, M. Zhu, W. Gao, J. Zhang and H. Shen analyzed. X. Wang, M. Ren and K. Gao wrote the article. L. Feng, C. Zhao and Z. Lin reviewed the article. All authors approved the final version for publication.
Conflict of interest
The authors disclose no actual or potential conflicts of interest.
Acknowledgment
We wish to thank all the authors of the included studies and especially Dr Shu-Yu Li and Dr Liselotte Gootjes for kindly sharing their unpublished data for inclusion in this meta-analysis.
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2020, Journal of Neuroscience MethodsCitation Excerpt :Post-mortem studies have linked altered properties of the corpus callosum to normal ageing (Hou and Pakkenberg, 2012) as well as neurological disorders including schizophrenia (Woodruff et al., 1995), multiple sclerosis (Evangelou et al., 2000), Huntington's disease and progressive supranuclear palsy (Mann et al., 1993). Recent magnetic resonance imaging (MRI) studies have strengthened these conclusions with evidence of atrophy (Goldman et al., 2017; Granberg et al., 2015; Lee et al., 2016; Wang et al., 2015a), morphological changes (Ardekani et al., 2014; Pardoe et al., 2015; Wolff et al., 2015), and demyelination in the human corpus callosum (Decker et al., 2018; Køster et al., 2018) and mouse models (Xiu et al., 2015). Diffusion tensor magnetic resonance imaging (DT-MRI or DTI) has also been applied to study the corpus callosum.
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2020, CortexCitation Excerpt :The hemispheres receive different but complementary input from the senses and are specialized for different yet interdependent functions (Ocklenburg & Güntürkün, 2018), so that both information transfer (E. Genc, Bergmann, Singer, & Kohler, 2011; Westerhausen, Gruner, Specht, & Hugdahl, 2009) and coordination of processing between the hemispheres (Davis & Cabeza, 2015; Thiel et al., 2006) is required in a lateralized brain. At the same time, altered structural and dysfunctional connectivity between the hemispheres has been reported for a significant number of developmental (e.g., Bradshaw, Bishop, & Woodhead, 2019; Dramsdahl, Westerhausen, Haavik, Hugdahl, & Plessen, 2012), psychiatric (e.g., Arnone, McIntosh, Tan, & Ebmeier, 2008; Whitford et al., 2011), or neurodegenerative conditions (e.g., Gootjes et al., 2006; Wang et al., 2015). A full understanding of any deviation in hemispheric lateralization in clinical conditions, consequently, also requires considering these prevalent differences in inter-hemispheric connectivity (e.g., Tréhout, Leroux, Delcroix, & Dollfus, 2017).
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2017, Human Movement ScienceCitation Excerpt :This perspective is crucial, given that disconnection is also seen in patients with mild cognitive impairments and early AD (Thomann, Wüstenberg, Pantel, Essig, & Schröder, 2006; Wang et al., 2006; Wang et al., 2015) and could contribute to the development of AD (Gold, Johnson, Powell, & Smith, 2012).
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These authors are co-first authors and contributed equally to this work.