Alzheimer’s disease is diagnosed primarily by clinical history. Multiple markers, including cerebrospinal fluid (CSF), neuroimaging, and genetic tests, are used as supportive findings (Jack et al., 2018). For the clinical diagnosis of AD, the following two criteria are used: (i) finding of major neurocognitive disorder (NCD), as defined by the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) (Eramudugolla et al., 2017), and (ii) the diagnostic guidelines of the National Institute on Aging and Alzheimer’s Association (NIA-AA) (Jack et al., 2018). The common item in the two criteria is slowly progressive dementia.

DSM-5 criteria make use of genetic risks, including family history and identification of the causative genes. Cognitive dysfunctions need to be shown in two or more cognitive domains centered on the memory/learning disorders. NIA-AA criteria also consider the genetic risks and clinical symptoms, similar to DSM-5. NIA-AA also takes into consideration evidence for the existence of AD pathology indicated by fluid biomarkers such as CSF and imaging biomarkers such as positron emission tomography (PET).

For research purposes, the International Working Group-2 (IWG-2) criteria for AD may be used (Dubois et al., 2014). The criteria broadly classify AD into typical AD, in which patients initially develop episodic memory impairment, and atypical AD, in which patients develop unique symptoms such as logopenic aphasia and frontal lobe signs. In addition, the evaluation of Aβ and tau, using CSF or PET, is also essential.

Mild cognitive impairment is a condition where slight cognitive impairment is recognized objectively compared with a condition before illness, but the patients’ activities of daily living are independent. MCI centered on memory deficits is clinically diagnosed as amnestic MCI (aMCI) presumably caused by AD pathology (Petersen et al., 1999, 2001). In addition, if there are findings from fluid/imaging markers indicating abnormalities in Aβ/tau, this condition is defined as AD-MCI (Albert et al., 2011).

Neuropsychological tests for AD are used for screening, differential diagnosis, and severity assessment. In clinical practice, screening for dementia or MCI is performed by tests for general cognitive functions. Mini Mental State Examination (MMSE), a simple test for assessing multiple cognitive domains, is used with a cutoff value of 23/24 for dementia (Trivedi, 2017). To diagnose MCI, Montreal Cognitive Assessment (MoCA) may be used with a cutoff value of 25/26 (Davis et al., 2015). These tests are helpful not only for screening through the assessment of total scores, but also for evaluating cognitive subcategories, including memory, attention, and visuospatial cognition.

As the disease progresses, patients with AD who initially had only memory impairment begin to show impairment involving multiple cognitive domains, including visuospatial cognition and language. Therefore, the Wechsler Adult Intelligence Scale (WAIS) and the Wechsler Memory Scale (WMS-R) are performed for a more detailed and comprehensive evaluation. However, because these advanced tests take more than 1 h, they are not suitable for screening.

Differential diagnosis to estimate the background pathology of dementia cannot solely rely on the neuropsychological examination, and is accordingly usually performed in combination with the assessment of clinical course and diagnostic imaging. In clinical practice, it is important to distinguish AD from other dementias such as DLB. Typical early symptoms are predominantly memory deficits in AD; executive function and visuospatial cognition are impaired only after memory deficits become apparent (Jack et al., 2018). Therefore, for differential diagnosis of AD, patients should undergo tests of memory, attention, executive functions, and visuospatial cognition to distinguish from DLB, in which executive function and visuospatial cognition are impaired at the early stage. However, because half of the cases of DLB are complicated by AD pathology, the possibility that DLB patients have AD-like memory impairment from the onset needs to be taken into account (Yoshizawa et al., 2013).

The severity assessment is essential for selecting rehabilitation and welfare services, and for longitudinal cognitive assessment in clinical research. Clinical Dementia Rating (CDR) (Morris, 1993) is a scale that evaluates general cognitive functions and community affairs, home/hobbies, and personal care. CDR yields a global scale (CDR-GS) and a sum of boxes (CDR-SB). CDR-GS is a general severity calculated by weighting the results of each subscale and is commonly used for disease staging. On the CDR, CDR-GS 0.5 corresponds to MCI, and 1.0 or larger corresponds to dementia (Chang et al., 2011). CDR-SB provides more information in terms of quantitative evaluation than CDR-GS, especially in MCI and early AD (O’Bryant et al., 2010). A feature of CDR is that it also refers to interviews with family members. Thus, CDR provides accurate information for understanding disease severity and significantly impaired function when evaluated by a proficient examiner that is well informed by the caregiver. Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-cog) is an assessment of 11 cognitive subcategories related to AD symptoms (Kueper et al., 2018). It is time-consuming, yet suitable for observing changes in cognitive function over time and is often used in clinical trials.

Most AD cases are sporadic, and the most prominent risk factor is aging (Angelova and Brown, 2019). AD also has familial cases, and many genetic risk factors are known. In autosomal dominant AD, mutations in proteins related to Aβ production, such as presenilin 1/2 (PSEN1/PSEN2) and amyloid precursor protein (APP) have been reported (Robinson et al., 2017). The mutation of PSEN1 accounts for the most common form of familial AD. PSEN1 is a component of the γ-secretase complex, which plays an essential role in the production of Aβ. APP mutations were first reported as an etiological factor in intracranial hemorrhage with amyloid pathology (amyloid angiopathy) and subsequently identified in familial AD cases. Patients with autosomal dominant AD (AD-AD) with PSEN1/PSEN2 and APP mutations may exhibit predominantly motor symptoms of akinetic-right type caused by Aβ deposition in the basal ganglia (Vöglein et al., 2019). Therefore, it may be difficult to distinguish some AD-AD cases from patients with PDD and DLB.

The ε4 allele of the APOE gene is the most important risk factor for sporadic AD (Jansen et al., 2015), being present in more than half of all AD cases, compared with a frequency of ∼14% in the general population (Pontifex et al., 2018). The ApoE protein functions as a ligand for the low-density lipoprotein (LDL) receptor family on the cell, and its abnormality causes atherosclerosis. In the brain, ApoE is mainly produced by astrocytes and is involved in transporting and removing cholesterol (Hauser et al., 2011). Furthermore, ApoE was reported to be involved in the degradation of Aβ (Jiang et al., 2008). Humans carry the ε2,ε3, and ε4 alleles with a worldwide frequency of 8.4, 77.9 and 13.7%, respectively (Liu et al., 2013). Compared with ε3/ε3, ε4 hetero carriers have an odds ratio (OR) of 2.8 [95% confidence interval (CI) 2.3–3.5], whereas homo carriers (ε4/ε4) have an OR of 11.8 (95% CI 7.0–19.8) (Pontifex et al., 2018). Conversely, APOEε2 is considered a protective factor for AD symptoms and pathology (Liu et al., 2013; Jindal and Bansal, 2016). APOEε4 has been reported to be involved in abnormal Aβ accumulation and perturbed functional connectivity (FC) (Sheline et al., 2010a).

Recent whole-exome sequencing and genome-wide association studies (GWAS) revealed a rare variant of the triggering receptor expressed on myeloid cells 2 (TREM2) in AD (Guerreiro et al., 2013; Jonsson et al., 2013). Interestingly, TREM2, which is expressed by microglia, could be involved in the removal of Aβ. It is expected that advances in genetic research will continue to reveal novel AD risk genes.

Aβ and tau, which constitute the hallmark senile plaques and NFTs, respectively, are the most important biomarkers of AD (Hampel et al., 2010; Olsson et al., 2016). The Alzheimer’s Disease Neuroimaging Initiative (ADNI) revealed that CSF can be used to detect the earliest changes in AD pathology (Weiner et al., 2012). Another study found decreased CSF-Aβ more than 25 years before the onset of AD (Bateman et al., 2012). These findings are incorporated into clinical diagnostic criteria, such as the NIA/AA criteria, for preclinical AD, which is the prodromal stage with normal cognition before MCI. As such, CSF-Aβ is considered a promising AD biomarker for early diagnosis, and is widely used in both the clinical and research laboratory settings. Lower Aβ_1–42 level and higher total tau (t-tau) or p-tau are reliable CSF biomarkers of AD pathology. Also, several reports suggest that the ratio of Aβ_1–42/Aβ_1–40 (Blennow and Hampel, 2003), t-tau/Aβ_1–42 (Shaw et al., 2009) or p-tau/Aβ_1–42 (De Meyer et al., 2010) more accurately reflect AD pathology than Aβ_1–42 or tau alone. In p-tau, CSF-tau phosphorylated at serine 181 (p-tau 181) is regarded as an AD-specific biomarker (Hampel and Teipel, 2004). As a CSF marker under development, Aβ oligomers might become a useful biomarker. Aβ oligomers are formed during the polymerization process of Aβ fibrils and are suggested to cause synaptic dysfunction. However, a hurdle to quantitative evaluation is the limited amount of Aβ oligomers in CSF (Hampel et al., 2010).

It has been suggested that plasma p-tau 181 is a potential AD-specific marker for predicting neurodegeneration and cognitive decline (Barthélemy et al., 2020; Moscoso et al., 2021). The results of a meta-analysis of blood biomarkers showed increased t-tau, p-tau 181, p-tau 217, neurofilament light protein (NFL) and decreased Aβ_1–42 in patients with AD and AD-MCI (Qu et al., 2021).

There are two widely used diagnostic criteria for PD—the United Kingdom Parkinson’s Disease Society Brain Bank (UKPDSBB) clinical diagnostic criteria (Gibb and Lees, 1988; Hughes et al., 1992) and the International Parkinson and Movement Disorder Society (MDS) clinical diagnostic criteria for PD (Postuma et al., 2015). Both require bradykinesia as a core symptom.

UKPDSBB criteria diagnose PD with the following three steps: presence of parkinsonism (step 1), exclusion of other parkinsonian syndromes (step 2), and existence of supportive features (step 3). These criteria are used worldwide and have had their accuracy verified by postmortem pathological diagnoses. The MDS diagnostic criteria include criteria for clinical diagnosis and research criteria for probabilistic prediction of PD before the diagnosis, according to risk factors and mild symptoms. Multiple studies have reported a high concordance rate for PD diagnosis between UKPDSBB criteria and MDS criteria (Malek et al., 2017; Postuma et al., 2018). Neither criterion mentions PDD, which requires an independent assessment (Sachdev et al., 2014).

In the PD spectrum, the MDS-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) is widely used for the evaluation of motor and non-motor symptoms (Goetz et al., 2008). MDS-UPDRS is divided into the following four sections: questionnaire for non-motor symptoms in daily life (Part 1), motor symptoms in daily life (Part 2), motor examination by a physician (Part 3), and questionnaire for motor complications (Part 4). The score is used for evaluation of disease severity and response to treatment.

Because of the difference in clinical symptoms, PD has been traditionally subjected to neuropsychological tests slightly different from those for AD. A condition consistent with MCI is found in nearly half of people with PD (Goldman et al., 2018). A longitudinal study reported that 80% of people with PD eventually show dementia (Williams-Gray et al., 2007). In PD, executive function is particularly vulnerable. Executive dysfunction is found from the prodromal stage and is more pronounced than memory impairment. Therefore, in the cognitive evaluation of PD, a test of frontal lobe functions such as Frontal Assessment Battery (FAB) (Dubois et al., 2000) is often added to a general cognitive test such as MMSE. Otherwise, it is recommended to perform MoCA, in which executive functions are tested in more detail than MMSE (Weintraub et al., 2015).

To assess cognitive dysfunctions in early PD (such as PD-MCI), it is recommended to examine five cognitive domains—memory, executive, attention/working memory, language, and visuospatial function (Litvan et al., 2012). To this end, thorough testing with WAIS and WMS may be considered (Tang et al., 2016).

In PDD, psychiatric symptoms are often directly linked to a decrease in Activities of Daily Living (ADL), and it is essential to evaluate the severity of psychiatric symptoms properly (Rosenthal et al., 2010). MDS-UPDRS Part 1 evaluates these symptoms. Questionnaires such as Beck Depression Inventory (BDI) (Beck et al., 1961), Questionnaire for Impulsive-Compulsive Disorders (QUIP) (Probst et al., 2014), and State-Trait Anxiety Inventory (STAI) (Caillava-Santos et al., 2015) are often used for the assessment of psychiatric symptoms in the PD spectrum.

Like AD, PD is primarily a sporadic disease. Multiple genetic and environmental factors appear to be involved in the onset of PD. Approximately 5% of PD cases are familial, some of which are inherited in a Mendelian fashion, including SNCA, PKRN, and LRRK2 (Singleton and Hardy, 2019). Studies of PD genetics have revealed some of the mechanisms including abnormal α-syn deposition, oxidative stress, mitochondrial dysfunction, and abnormalities in the ubiquitin-proteasome system leading eventually to cell death (Ganguly et al., 2021). Genome-wide association studies (GWASs) have identified many single nucleotide polymorphisms (SNPs) as risk factors for PD (Nalls et al., 2014; Blauwendraat et al., 2020). However, it is still unclear how and to what extent each of these genes is related to PD pathology.

GBA has recently been recognized as a risk gene for PD. GBA is the causative gene of Gaucher’s disease, an autosomal recessive lysosomal disorder caused by glucocerebrosidase (GBA) deficiency (Hruska et al., 2008). It is reported that decreased GBA activity increases soluble α-syn oligomers (Mazzulli et al., 2011). Interestingly, decreased GBA activity in the brain is also seen in patients with sporadic PD (Gegg et al., 2012). The OR for any GBA mutation in PD patients vs. controls was reported to be 5.43 (Sidransky et al., 2009). Carriers of GBA mutations are reported to represent 4–29% of patients with PD, and GBA may be related to the early onset of PD (Lees et al., 2009).

There are few established serum biomarkers for PD. However, several reports have pointed out the association between PD and inflammatory markers. For example, a study found a correlation between the development of sporadic PD and elevated inflammatory biomarkers such as serum TNF-α and IL-6 (Ferrari and Tarelli, 2011). Another study found that decreased apolipoprotein A1 and increased C-reactive protein (CRP) were associated with severe motor symptoms, depression, and sleep disorders (Ferrari and Tarelli, 2011; Lawton et al., 2020).

The most straightforward fluid biomarker should be the direct measurement of α-syn. However, there are some difficulties in measuring the protein in body fluids. Blood has the advantage of easy collection, but simple measurement of α-syn using a blood sample is not currently helpful in distinguishing PD from healthy population. Although there are multiple reports of elevated plasma or serum α-syn levels in PD patients compared with healthy people (Chang et al., 2020), reports on the correlation between α-syn levels and clinical symptoms are inconsistent. There is even a report of decreased plasma α-syn in PD patients (Li et al., 2007). Technical problems may underlie the discrepancies, including contamination of α-syn from erythrocytes due to hemolysis, differences in assay technique, and differences in the binding ability of the antibodies to α-syn (Ganguly et al., 2021). More recently, α-syn oligomers and aggregated α-syn have drawn attention as biomarkers for the progression of PD (Maass et al., 2019). The α-syn oligomers and aggregated α-syn may be involved in the transcellular transmission of α-syn (Okuzumi et al., 2021).

Measurement of α-syn in CSF should be established. Many studies have reported that total α-syn is lower in patients with PD than in healthy controls (Tokuda et al., 2006; Mollenhauer et al., 2013; Wennström et al., 2013). However, the specificity of this finding is not high enough to distinguish PD from healthy controls or from other parkinsonian syndromes (Zhou et al., 2015). Measurement of phosphorylated α-syn or the ratio of phosphorylated α-syn to total α-syn might be helpful in distinguishing PD from healthy controls and from other parkinsonian syndromes (Majbour et al., 2016). CSF studies also show an increase in α-syn oligomers and an increase in the ratio of α-syn oligomer to total α-syn in PD (Tokuda et al., 2010; Majbour et al., 2016; Kakuda et al., 2019). The development of measurement methods has seen recent progress (Kakuda et al., 2019; Okuzumi et al., 2021). Recently, real-time quaking-induced conversion (RT-QuIC), a technique originally developed to detect the self-aggregating ability of proteins in prion diseases, was used for detecting α-syn aggregation with a sensitivity of 92% (DLB) and 95% (PD), and a specificity of 100% (Fairfoul et al., 2016). The use of the novel assays, including RT-QuIC and α-syn oligomers, currently promises the most useful biomarker to precisely capture synuclein deposition.

Till now, AD and PD have been studied independently, and most findings have been obtained independently.

In AD, memory impairment identified by clinical interview and neuropsychological testing is the core clinical symptom. Responsible genes for familial cases and risk genes for sporadic cases have been found, but more are yet to be identified. The identification of reliable biological or imaging markers for sporadic cases is of great importance in AD research to advance the development of disease-modifying therapies. Blood biomarkers are under development, and promising methods have been reported (Nakamura et al., 2018). Measurements of CSF can reveal alterations in key proteins (Aβ and tau) underlying AD pathology. To visualize the spatial distribution of Aβ, amyloid-PET has been established for its utility in clinical studies. A few tracers have been proposed for tau-PET. Structural MRI and perfusion SPECT/FDG-PET are used to detect brain atrophy and reduced blood flow/energy requirement, respectively, both of which indicate neuronal loss. The analysis of brain connectivity by rsfcMRI consistently shows abnormality in the DMN. Moreover, ML combined with FC has permitted discrimination between AD and healthy controls with a degree of accuracy.

Parkinson’s disease is characterized by movement disorder possibly complicating cognitive impairment. In cognitive disturbance in PD, executive function is particularly impaired. Many responsible genes for familial cases and risk genes for sporadic cases have been identified. MIBG-SPECT and DAT-SPECT are widely performed to detect the neurodegeneration of monoaminergic neuronal systems, which is a hallmark of PD pathology. Structural MRI with high magnetic field has utility in PD research for revealing the iron deposition and neurodegeneration in the SNc, which are the core pathological changes in PD. Detection methods for the other pathological hallmark, α-syn, are not established yet. Imaging measures of brain atrophy and blood flow/energy requirement show variable findings, depending in part on the motor subtype and presence of cognitive decline. Decreased perfusion is shown by SPECT in PD with cognitive decline. A group analysis using structural MRI revealed cerebral atrophy, especially in patients with substantial non-motor symptoms. Many rsfcMRI studies are reported, but again, the results vary, depending on the subtype and stage. Yet, network abnormalities of the basal ganglia, frontal lobe, and cerebellum, corresponding to movement disorders, and DMN/frontal lobe abnormalities corresponding to cognitive disorders have been consistently reported.

Because AD and PD are regarded distinct and independent disorders with different core clinical symptoms (cognitive vs. motor), different neuropsychological tests and imaging methods have been traditionally applied to each disease. Unfortunately, different evaluation methods have been applied to symptoms common to AD/PD as well, including cognitive and psychiatric symptoms. Therefore, it remains unclear which pathophysiological mechanisms are common, and which are unique to each disorder, despite the shared symptoms and the putatively common pathological mechanisms.

Now, it is recognized that patients in the PD spectrum may show AD-like symptoms, and those in the AD spectrum could also exhibit PD-like symptoms, including anosmia and movement disorders. Anosmia is present in both AD and PD. Patients with AD were reported to perform worse than patients with PD in an odor recognition task (Lehrner et al., 1997). Anosmia in AD is common in cases with Lewy body accumulation in the postmortem brain (Olichney et al., 2005). However, it is not clear how α-syn, which constitutes Lewy bodies, is involved in olfaction problems in AD, in contrast to PD, in which neuronal loss occurs in the locus coeruleus, the raphe nuclei, and the nucleus basalis of Meynart (Doty, 2012).

In contrast to the common clinical conception, patients with PD may present with AD-like memory-dominant cognitive decline (Das et al., 2019). Conversely, some patients with AD may display parkinsonism (Sasaki, 2018). In a longitudinal study of AD with a mean follow-up of 3.6 years, 12.3% of patients had clinically apparent parkinsonism at the initial visit, and 22.6% at the final visit (Portet et al., 2009). Interestingly, patients with AD complicated with mild parkinsonism exhibit decreased DAT in the caudate nucleus, the pattern of which is closer to DLB than PD (Chung et al., 2019). To our knowledge, no study has directly examined prodromal PD pathology in AD with motor symptoms using CSF-α-syn or α-syn-PET. BPSD with hallucinations, suggestive of monoamine system dysfunction, a hallmark of PD pathology, is also present in AD.

Similarities in clinical manifestations are clearly seen between AD and PD. Anosmia may point to the common pathophysiology underlying the two. However, since few, if any, large-scale studies used a common clinical scale to compare AD and PD, it is unclear exactly how similar or different are these common symptoms in the two disorders.

Mounting evidence indicates α-syn pathology in the AD spectrum and Aβ/tau pathology in the PD spectrum. In animal experiments, many forms of interactions between AD-related proteins and PD-related proteins are reported, and such molecular interactions may explain the mixed AD and PD pathology common in humans. It has been suggested that Aβ directly affects the toxicity of α-syn. Aβ-rich AD-transgenic mice, including PSEN1/2 and APP mutation, show rapid and widespread aggregation of α-syn (Bassil et al., 2020). This finding suggests that Aβ plaques may promote seeding and spreading of α-syn. Tau and α-syn directly interact with each other to promote co-assembly (Bassil et al., 2021). Thus, each abnormal protein promotes the formation of other abnormal proteins.

Most postmortem brains in patients with PDD are complicated by varying degrees of AD pathology (Dickson et al., 2017). Thus, it is widely accepted that a certain degree of PD cognitive impairment may be associated with AD pathology. In AD, however, approximately 33% of the patients exhibit significant Lewy body pathology alongside Aβ (Deture and Dickson, 2019), especially the presence of Lewy pathology in the olfactory bulb and the amygdala in patients with AD (Serrano-Pozo et al., 2011; Deture and Dickson, 2019). Also, patients in the PD spectrum show accompanying AD pathology (Kosaka and Manabe, 2010). A multicenter-cohort study of DLB examined Aβ and p-tau with CSF and PET. Among the patients, 32% were Aβ positive, 13% were p-tau positive, and 15% were positive for both Aβ and p-tau (Ferreira et al., 2020). Aβ and p-tau were found to be independent risk factors for cognitive decline in DLB. Interestingly, in the same study, p-tau was associated with a lower likelihood of parkinsonism and RBD in DLB.

While amyloid pathology in PDD/DLB is common, the complications of amyloid pathology in non-demented PD remain unclear. In a study using amyloid-PET, the prevalence rate of Aβ was rather low in non-demented PD patients compared with the healthy population (Mashima et al., 2017). It is possible that the involvement of Aβ pathology may be different in non-demented PD and PDD/DLB, but further studies are needed.

Because interactions of abnormal proteins in advanced stages of AD/PD-related dementia may occur, it is important to examine overlapping or interacting proteinopathies even in the preclinical and prodromal stages. In aMCI, which is considered a prodromal stage of AD, patients show cognitive decline that correlates with increased CSF α-syn levels (Twohig and Nielsen, 2019). Although few studies have evaluated protein interactions in prodromal PD, the coexistence of Aβ and tau pathology may promote the progression of prodromal PD to DLB (Berg et al., 2021). Protein interactions in prodromal AD and PD remain unclear, and this requires research attention. Considering that the therapeutic target of each disease is shifting to the early stage, a more thorough understanding of the disease, including protein interactions in the preclinical/prodromal stage, is urgently needed.

Cerebrovascular lesions cause cognitive or motor impairment. However, the significance of vascular lesions including white matter hyperintensities (WMHs) is often underestimated in the pathogenesis of dementia in comparison with, e.g., the proteinopathy viewpoint (Prins and Scheltens, 2015). In the AD spectrum, multiple studies indicated the presence of vascular lesions as an important background pathology (Prins and Scheltens, 2015; Saito et al., 2015; Muddapu et al., 2020). Decreased amyloid clearance has been proposed for the involvement of WMHs in AD pathogenesis (Saito et al., 2015). WMHs are also considered to underlie dementia in PD (Bohnen and Albin, 2011; Liu et al., 2021).

In AD, many abnormalities in connectivity centering on the hippocampus and DMN, related to Aβ pathology, have been reported (Takamura and Hanakawa, 2017). In PD, abnormalities in the motor networks, including the basal ganglia and SMN, have been consistently reported. Importantly, DMN abnormalities have been observed in both the AD and PD spectra with cognitive impairment. AD features abnormalities mainly in the precuneus and PCC within the DMN (Ibrahim et al., 2021) whereas DLB has more widespread network abnormalities involving the DMN and the occipital lobe (Wolters et al., 2019).

It seems important to investigate the relationship between proteinopathic abnormalities and neuropsychological indices in the AD and PD spectra, and their relationship with the neurocircuitopahty viewpoint, including the DMN abnormality. Existing MRI cohort studies do not allow us to perform such an analysis because they recruit AD and PD cohorts independently with different evaluation metrics. Therefore, it remains unknown how Aβ, tau and α-syn are involved in the differences in DMN abnormalities between AD and PD. By performing network analysis using biomarkers of both AD and PD, it may be possible to assess the degree to which each AD/PD pathology contributes to network changes. In particular, little is known about the relationship between PD network abnormalities and proteinopathy.

Although the presence of substantial overlaps has been demonstrated, AD and PD cohorts have been recruited and evaluated separately. In particular, there is a marked discrepancy between the evaluation metrics for cognitive and neuropsychiatric functions, which limits the utility of existing cohorts for comparing AD and PD. Therefore, we recommend that a new cohort should be designed to resolve this problem. Each AD/PD spectrum should be evaluated throughout the preclinical, prodromal, early, and advanced stages.

Accumulating evidence indicates that the comorbidity of AD and PD pathology is common in elderly people. Many questions remain unanswered. For example, it is not yet known if the comorbidity is simply coincidental to the aging process per se or if it is caused by the interactions of the key common molecules in AD and PD. It is not clear when and how the mixed AD and PD pathology (i.e., co-pathology) emerges and progresses. Furthermore, it is unclear how the co-pathology affects clinical symptoms, brain structure, and circuit pathology.

To answer these questions, we should first consider merging data from existing cohorts targeting a single disease spectrum for efficiency. There are renown MRI cohorts including ADNI and PPMI (Petersen et al., 2010; Marek et al., 2011; Weiner et al., 2017; Veitch et al., 2019), which have evaluated the AD and PD spectrum separately in depth. For biomarker/genetic studies, it may be worthwhile evaluating a common set of proteins or risk genes from originally distinct cohort. However, it is known that technical differences such as a way to collect, store and measure samples may limit the reliability of the results. For imaging studies, it is still difficult to perform accurate statistical analysis of MRI data obtained from different MR scanners and protocols (Duchesne et al., 2019). Although image analysis methods to remove the scanner difference have been proposed, such methods will remove the difference across the disease spectra when AD is heavily sampled in some sites and PD is heavily sampled in the others. Also, since each cohort tend to focus on spectral-specific clinical and neuropsychological indices, it is difficult to compare network indices correlated with a common set of neuropsychological indices. Recently, some PD cohorts employed amyloid and tau PET. It is intriguing to compare spatial distribution of pathological protein across spectra; however, it is still difficult to associate the findings with neuropsychiatric indices with different batteries.

Considering the homology of AD and PD spectra in terms of abnormal proteins, vascular factors, motor sand cognitive symptoms, it is most reasonable to perform the same elaborate evaluation in a single cohort employing the same evaluation measures across the spectra. Here we propose to establish a newly designed cohort combining both the AD and PD spectra (Figure 2). Then, symptoms, biomarkers, brain structure and circuit pathology should be evaluated. The unified AD and PD cohort is expected to overcome the limitation of the existing AD cohort and PD cohort combined.

To investigate the similarities and differences between AD and PD, it is important to design a cohort study with a few key characteristics. First, it is necessary to apply the same behavioral, cognitive, and neuropsychiatric scales to AD and PD. Behaviorally, because parkinsonism is sometimes seen in AD, it may be needed to evaluate motor functions of both AD and PD with MDS-UPDRS. CDR and ADAS, which are used for AD, and FAB and MoCA, which are used for early PD, may need to be jointly used. It would be interesting to compare the results between PD-MCI in AD-MCI with tests for the five cognitive domains. Unified questionnaire must be used for assessment of behavioral and psychiatric symptoms such as anxiety, depression, and sleep problems, which are common to both AD and PD. The same olfactory test should be applied to AD and PD to test anosmia.

Next, Aβ/p-tau and α-syn should be evaluated for both AD and PD. To date, Aβ and p-tau have been mostly measured for AD only, and α-syn has been measured for PD cohorts only. The same assay methods of these proteins should be applied to blood and spinal fluid obtained from AD and PD. In postmortem brain research, NFTs appear in the locus coeruleus and entorhinal cortex first, and then spread to the limbic system and neocortex as dementia progresses (Braak and Braak, 1991). To evaluate the spatial distribution of abnormal protein accumulations over time, longitudinal PET imaging is important.

Finally, network and structural abnormalities of the brain should be compared in detail, preferably using the HCP-style MRI protocol (Glasser et al., 2016; Koike et al., 2021). This modern MRI technology should be combined with the standardized assessment of clinical scales and proteinopathies with fluid biomarkers and SPECT/PET. The effects of Aβ, tau and α-syn accumulation on network connectivity and their association with symptoms should be examined.

Here are cases where the unified AD and PD cohort may provide new knowledge. Clinically, aMCI is often presumed to have AD pathology (Petersen et al., 1999, 2001). The unified AD and PD cohort will provide data to suggest how common is aMCI without evidence for AD pathology but, for example, decreased DAT indicative of the PD spectrum (presumably prodromal DLB). Then, markers of the circuit pathology of such non-AD MCI may be compared with those of PD-MCI “neuroimaged” in the cohort to examine if the markers of neurocircuitopathy could serve as early markers differentiating AD-MCI and non-AD MCI. In the unified AD and PD cohort, vascular burden as a common background pathology to AD and PD can be evaluated from multiple perspectives, such as interactions of WMH burden with the proteinopathy, symptoms or circuit pathology. We can then ask how WMH burden similarly or differentially modulates manifestation of the AD spectrum and PD spectrum. The unified AD and PD cohort may also clarify the specific roles of the DMN in cognitive decline in the AD spectrum and PD spectrum since DMN is implicated for both AD and PDD.